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PubMed2026

Transcranial Alternating Current Stimulation at 40 Hz Improves Social Functioning in Children With Autism Spectrum Disorder: A Randomized Clinical Trial.

BACKGROUND: Autism spectrum disorder (ASD) lacks rapid and effective interventions for its core social difficulties. The right temporoparietal junction (rTPJ), a critical hub for social cognition, together with gamma band abnormalities implicated in ASD, provides a promising neuromodulation target. METHODS: In this randomized, double-blind, sham-controlled trial, 47 children with ASD (39 male; mean [SD] age = 8.79 [2.71] years) were assigned to receive either 21 sessions of 40-Hz high-definition transcranial alternating current stimulation (tACS) targeting the rTPJ (3 sessions/day for 7 days) or sham stimulation, with assessments conducted at baseline, postintervention (week 1), and a 3-week follow-up (week 4). The primary outcome was change in Ohio State University Autism Rating Scale-DSM-5 (OARS-5) total scores. Secondary outcomes included the Aberrant Behavior Checklist-Second Edition, Social Responsiveness Scale-Second Edition, and Short Sensory Profile. Eye-tracking metrics during Frith-Happé animations were exploratory measures of theory of mind (ToM)-related social cognitive processing. RESULTS: The active group demonstrated significant improvements in OARS-5 total scores at week 1 (mean difference = -1.13, 95% CI [-1.78 to -0.47], p < .001) and week 4 (mean difference = -1.47, 95% CI [-2.20 to -0.74], p < .001). Improvements in selected behavioral and sensory domains were observed. Average fixation duration during ToM animations showed a significant group × time interaction. No serious adverse events occurred. CONCLUSIONS: These findings suggest that 40-Hz tACS targeting the rTPJ may be associated with rapid improvements in ASD symptom severity, particularly social functioning, in children with ASD, while being well tolerated. Clinical significance requires further evaluation.

Humans↗

PubMed2026

Divergent responses of the gill, hepatopancreas, and eyestalk to acute alkalinity stress in Penaeus vannamei: Osmoregulatory compromise, metabolic trade-off, and endocrine disruption.

The expansion of aquaculture into inland saline-alkali waters is constrained by high carbonate alkalinity (CA), a severe environmental stressor for crustaceans. However, the systemic molecular mechanisms underlying its lethal toxicity remain poorly understood. In this study, we employed a comparative transcriptomic approach to investigate the tissue-specific responses of Pacific white shrimp, Penaeus vannamei, under acute lethal stress (48 h-LC50). We focused on three functionally distinct organs: the gill, hepatopancreas, and eyestalk. The results revealed a systemic but highly tissue-specific transcriptomic response. The gill, as the primary interface, exhibited severe structural impairment and critical failure of osmoregulation, highlighted by the significant downregulation of delta-1-pyrroline-5-carboxylate synthetase (P5CS). In contrast, the hepatopancreas initiates a profound metabolic trade-off, sacrificing growth-related pathways to bolster a robust antioxidant defense system, as evidenced by the activation of sulfur metabolism and high protein turnover. The eyestalk displayed a striking disconnect between hyperactivated stress signaling pathways (e.g., mTOR/FoxO) and the collapse of its protein secretory machinery, marked by the suppression of the ER translocon component Sec61. Collectively, our findings suggest that lethal alkalinity toxicity in P. vannamei results from systemic collapse driven by a complex interplay of osmoregulatory failure, metabolic trade-offs, and endocrine disruption. This study provides a comprehensive molecular snapshot of an organism at its physiological limit, offering novel insights into the adaptive strategies and ultimate tolerance boundaries of crustaceans in extreme environments.

Animals↗

PubMed2026

Artificial Intelligence Technologies in Nursing Clinical Decision-Making: An Umbrella Review.

AIM: To describe contemporary peer-reviewed literature on artificial intelligence in nurses' clinical decision-making. METHODS: An umbrella review of literature reviews. DATA SOURCES: Four major databases were searched for reviews published between 2019 and 2024. RESULTS: Sixteen literature reviews reported on 965 nursing artificial intelligence primary studies. The studies focused on technology development and emerging performance evaluations, whilst real-world testing or implementation in nursing clinical settings was rare. Rigorous comparative analyses were lacking. While artificial intelligence demonstrates promise in decision-making, challenges such as a lack of controlled studies, algorithmic bias, limited reproducibility and insufficient clinical trials hinder its practical impact. Ethical concerns, transparency and patient data privacy issues pose barriers to AI integration in nursing practice. Ethical and legal guidelines for patient privacy are needed and should be taught along with AI literacy training for nurses. CONCLUSIONS: Artificial intelligence has the potential to enhance clinical nursing decision-making, although evidence is limited by too few examples of nurse participation during development. Underutilisation in administrative nursing functions hinders implementation. Nurses should assume a central role in the design and development of AI applications to ensure that these technologies address the realities of nursing practice. With such improvements, artificial intelligence can transform nursing practice, improve nurses' clinical decision-making and ultimately enhance consumer healthcare outcomes. PATIENT OR PUBLIC INVOLVEMENT: No Patient or Public Involvement. REPORTING METHOD: While there is no reporting checklist for umbrella reviews, the PRISMA guide for systematic reviews was followed.

Artificial Intelligence↗

PubMed2026

The application of artificial intelligence in healthcare practice: A mapping review of systematic reviews.

Artificial intelligence (AI) is rapidly transforming healthcare practice, with growing evidence supporting its use in diagnosis, prognosis, treatment planning, and operational decision-making. The proliferation of systematic reviews in recent years underscores the need for an updated synthesis of the literature to inform research, policy, and practice. We searched PubMed, Web of Science, Scopus, IEEE Xplore, and CINAHL for systematic reviews and meta-analyses published between 2019 and February 2026. Eligible reviews focused on AI applications in healthcare practice, were peer-reviewed, and written in English. A total of 368 reviews met the inclusion criteria. Publication volume increased steadily, peaking in 2025. AI research was concentrated in high-density domains, such as radiology, oncology, and critical care. Across reviews, diagnostic imaging, electronic health record (EHR) data, and biomarkers/laboratory results accounted for 68% of training data sources, though newer data types, such as wearable device and sensor data, emerged from 2022 onward. Diagnosis, prognosis, and treatment comprised over 80% of AI applications, with novel uses emerging in recent years, such as AI-assisted clinical documentation (e.g., ambient documentation tools) and patient education. Ethical concerns were reported in 78.5% of reviews, with privacy, model accuracy, data and algorithmic bias, and explainability as recurrent themes. The proportion of reviews reporting ethical concerns increased from 2021 to 2025. AI applications in healthcare are expanding in scope, diversifying in data sources, and evolving toward novel clinical and operational uses. The human-centered AI or augmented intelligence paradigm, integrating computational precision with clinical expertise, holds significant promise but will require parallel advances in governance, regulatory frameworks, and ethical oversight to ensure safe adoption.

Artificial Intelligence↗

PubMed2026

ScRNA-seq analysis reveals the effects of nitrite stress on the endocrine system of the eyestalk in Litopenaeus vannamei.

Nitrite is a harmful substance generated in Litopenaeus vannamei farming systems, largely originating from the inadequate breakdown of surplus feed and shrimp feces. Its accumulation in the water can affect the growth and physiological functions of shrimp, damage the immune system, and even cause mass mortality, thus becoming a key environmental factor restricting the green development of the industry. Under nitrite stress, the eyestalk, as an important neuroendocrine regulatory center in crustaceans, participates in the stress adaptation of the organism and exerts a protective effect by regulating energy metabolism and immune function. However, the molecular regulatory mechanism of the eyestalk in response to nitrite stress remains unclear. In this study, single-cell RNA sequencing (scRNA-seq) technology was used to analyze the heterogeneity of eyestalk cells in L. vannamei under nitrite stress. A total of 18, 394 high-quality cells were obtained, and six major cell subpopulations, including Neurosecretory cell, Motor neuron, Sensory neuron, Interneuron, Neurogliocyte, and Support cell, were identified. Differential expression analysis identified 839 differentially expressed genes, and different cell types showed distinct specific responses to nitrite stress. Functional enrichment analysis indicated that pathways such as glycolysis, oxidative phosphorylation, ribosome function, and endoplasmic reticulum protein processing were significantly activated, while signal transduction and DNA repair-related pathways were inhibited. Further analysis revealed that nitrite stress could induce mitochondrial function changes and trigger oxidative stress, thereby affecting the neuroendocrine system function of the eyestalk. This study provided insights into transcriptomic responses of the eyestalk to nitrite stress at the single-cell level, laying a theoretical foundation for the management of aquaculture environments.

Animals↗

PubMed2026

Astigmatic vector outcomes after FS-LASIK versus SMILE for high myopic astigmatism: a single-center retrospective comparative cohort study without cyclotorsion compensation.

PURPOSE: To compare astigmatic correction vector outcomes between femtosecond laser-assisted in situ keratomileusis (FS-LASIK) and small-incision lenticule extraction (SMILE, also termed Keratorefractive Lenticule Extraction, KLEx) without intraoperative cyclotorsion compensation in patients with high myopic astigmatism (- 2.00 to - 3.75 D), and to clarify procedure-specific correction tendencies under this non-standardized alignment protocol. METHODS: This single-center retrospective comparative cohort study enrolled 155 eyes (one eye randomly selected per patient) that underwent FS-LASIK (80 eyes) or SMILE/KLEx (75 eyes) for high myopic astigmatism correction from January 2023 to July 2024 in Beijing Fenglian Jiayue Lige Clinic. Intraoperative cyclotorsion compensation was intentionally disabled to isolate inherent procedural astigmatism correction characteristics. Standardized Alpins vectorial analysis was performed at 3 months and 12 months postoperatively. PRIMARY ENDPOINT: 12-month Alpins correction index (CI). Multivariable propensity score adjustment was applied to mitigate confounding by clinical treatment selection bias. Statistical multiplicity control was implemented for secondary vector and visual outcomes. RESULTS: Baseline demographic, refractive, corneal and ocular biometric parameters were balanced between groups after propensity matching. No statistically significant intergroup differences were detected in uncorrected distance visual acuity (UDVA), corrected distance visual acuity (CDVA), residual cylinder, safety index or efficacy index at 3 and 12 months (all P > 0.05). Under the non-cyclotorsion-compensated protocol, significant intergroup differences were identified in the magnitude of surgically induced astigmatism (SIA), correction index (CI), and magnitude error (ME) at both follow-up timepoints (all P < 0.0001). Target induced astigmatism (TIA), difference vector (DV), index of success (IOS), and angle error (AE) magnitudes were comparable between groups (all P > 0.05). The vector mean axis of DV differed significantly between groups at 3 and 12 months (Watson-Williams circular test, all P < 0.0001). No reoperations were documented in clinic medical records for either cohort. No standardized dry eye questionnaires, tear film testing or corneal nerve density metrics were collected to quantify dry eye adverse events; only unstructured clinical notes were reviewed for complication screening. CONCLUSIONS: Under surgical alignment without cyclotorsion compensation, FS-LASIK and SMILE/KLEx both yielded acceptable visual and refractive safety/efficacy for high myopic astigmatism (- 2.00 to - 3.75 D) at 1-year follow-up, but demonstrated divergent astigmatism correction tendencies: FS-LASIK exhibited relative astigmatism overcorrection (vector mean DV: - 0.35 ± 0.43 D × 91°, CI > 1), while SMILE/KLEx showed relative undercorrection (vector mean DV: - 0.21 ± 0.53 D × 12°, CI < 1). These correction biases are specific to the study's manual limbal alignment protocol without cyclotorsion tracking and cannot be generalized to modern optimized surgical platforms equipped with automated cyclotorsion compensation. Residual refractive errors across both groups are likely multifactorial, including differential corneal stromal healing responses, divergent femtosecond/excimer laser tissue modification mechanisms, and uncorrected intraoperative ocular cyclotorsion.

Humans↗

PubMed2026

Identifying and Prioritizing Core Components of Relationship Education Programs: a Case Study of an Artificial Intelligence (AI) Assisted Systematic Review.

The field of prevention science seeks to identify and implement effective strategies to address social, emotional, and health challenges. A critical aspect of this endeavor is determining the core components of prevention programs that drive positive outcomes. This article presents a case study utilizing artificial intelligence (AI)-assisted systematic review methods to identify key components of healthy marriage and relationship education programs. Given the growing body of research in this domain, AI tools offer a promising means to enhance the efficiency and accuracy of literature reviews. This study employed AI to screen, code, and validate research articles, demonstrating its effectiveness in expediting systematic reviews while maintaining high accuracy in inclusion screening. This case study involved a systematic review of 22,028 resources (identified from PsycINFO, Academic Search Ultimate, and Google) and a final data set of 268 relevant studies. AI screening was integral in effectively conducting multiple rounds of screening. However, findings also highlight challenges in AI-assisted qualitative data abstraction, underscoring the continued need for human expertise in complex coding tasks. The study contributes to the ongoing discourse on integrating AI into prevention science methodologies and offers insights for optimizing AI applications in systematic reviews.

Artificial Intelligence↗

PubMed2026

Artificial intelligence for dental caries detection: An umbrella review.

Artificial intelligence (AI) has been proposed as a tool to improve dental caries detection across imaging modalities; however, its clinical value remains uncertain. This umbrella review aimed to synthesize and critically appraise systematic reviews evaluating AI for caries detection and diagnosis. An umbrella review was conducted following PRIOR guidance (PROSPERO CRD420261340728). Searches were performed in MEDLINE, Embase, Scopus, Web of Science, and Google Scholar up to 15 March 2026. Methodological quality was assessed using AMSTAR 2, and overlap of primary studies was quantified using the corrected covered area (CCA). Seventeen systematic reviews were included, of which five reported diagnostic test accuracy meta-analyses using bivariate or HSROC models. Across these meta-analyses, pooled sensitivity ranged from 0.76 to 0.94 and specificity from 0.85 to 0.91. Most systems were based on deep learning models applied to bitewing radiographs and intraoral photographs. However, substantial heterogeneity was observed in imaging modalities, lesion thresholds, analytical tasks, and evaluation metrics. In addition, a high degree of overlap across reviews and recurrent methodological limitations, including reliance on retrospective datasets, limited external validation, and inconsistent reporting, substantially weaken the reliability of the evidence. Although AI models demonstrate high diagnostic performance under experimental conditions, current evidence does not support their use as stand-alone diagnostic tools. Their clinical applicability remains limited, and implementation should be restricted to decision-support contexts until robust prospective validation demonstrates meaningful impact on clinical decision-making and patient outcomes.

Dental Caries↗

PubMed2026

Efficacy of current approaches to non-invasive diagnosis of skin cancer and the potential impact of artificial intelligence: A systematic review and meta-analysis.

BACKGROUND: Skin cancer is one of the most prevalent malignancies worldwide, particularly within Caucasian populations. This systematic review and meta-analysis aimed to quantitatively review the current literature on non-invasive diagnosis of skin cancer and evaluate the current evidence to support the use of tools in addition to, or in replacement of clinician face-to-face assessment. METHODS: A literature search was conducted for publications in PubMed, Medline and Embase databases. Articles describing accuracy, sensitivity, specificity and outcomes of their mode of assessment were included. A total of 208 articles met the inclusion criteria. RESULTS AND CONCLUSION: This systematic review and meta-analysis showed that the diagnostic performance of artificial intelligence (AI) in the interpretation of dermatoscopic images was high for melanoma diagnosis, basal cell carcinoma or malignancy, in comparison to dermatoscopic assessment alone by clinicians and experts. Although AI interpretation of images demonstrated higher sensitivity for melanoma diagnosis in comparison to clinical assessment combined with dermatoscopic assessment, it is unclear if this is also the case for basal cell carcinoma and squamous cell carcinoma diagnosis. Reflectance confocal microscopy, a non-invasive high resolution imaging technique, is known to have a high sensitivity for diagnosing cutaneous malignancy, and this may have applications within secondary care. Therefore, AI could help reduce resource burden and aid in clinical assessment, particularly within primary care settings.

Humans↗

PubMed2026

A systematic approach to standardizing the visual appearance of endometriotic lesions for artificial intelligence recognition.

INTRODUCTION: Numerous studies have shown that the diagnostic performance and reproducibility of visual recognition of endometriosis during laparoscopy are poor. The use of artificial intelligence (AI) seems relevant for exhaustive lesion recognition. Standardization of the visual classification of lesions, in the form of an ontology, is an essential prerequisite to enable medical experts to annotate surgical data consistently and subsequently allow engineers to train and build an artificial intelligence tool for endometriosis recognition. MATERIAL AND METHODS: A systematic search was conducted in the MEDLINE (via PubMed), EMBASE, and the Cochrane Library databases up to May 2022, aiming to identify studies describing the laparoscopic visual appearance of superficial endometriosis, endometriomas, and deep infiltrating endometriosis. The accumulated data in the literature concerning the visual appearance of the different forms of endometriosis were used to create an ontology that could be used for artificial intelligence applications. RESULTS: Out of 932 articles screened, 35 studies were selected based on the inclusion criteria of human subjects with histologically confirmed endometriosis lesions visualized via laparoscopy. The selected studies were reviewed to develop a visual ontology of endometriosis lesions observed via laparoscopy. The lesions were categorized into 4 classes and further subdivided into 11 subclasses: superficial (black, red, white, or subtle), adhesions (dense or filmy), deep (obliteration, retraction, or deformation), and ovarian (endometrioma or chocolate fluid). The positive predictive value (PPV) varied across lesion types: black lesions (PPV 47%-97%), red lesions (PPV 33%-100%), white lesions (PPV 20%-81%), and ovarian endometriosis (PPV 42%-98%). Nonspecific lesions such as adhesions (PPV 16%-50%) and subtle superficial lesions (PPV 0%-67%) presented lower PPVs. Deep endometriosis lesions, often buried within organs, required indirect signs (obliteration, retraction, deformation) for identification. CONCLUSIONS: The visual ontology proposed in this systematic search could facilitate the detection and classification of endometriosis lesions using artificial intelligence. This study highlights the challenges of reaching a consensus on lesion recognition and classification in AI projects due to the diverse visual presentations of endometriosis.

Humans↗

PubMed2026

Artificial intelligence in treatment prediction for skeletal Class III malocclusion: A systematic review.

In skeletal Class III patients, treatment options range from orthodontics to orthognathic surgery. Choosing the optimal approach requires a comprehensive clinical evaluation, which may be supported by AI tools. The aim of this study was to assess the performance of AI models in predicting the need for orthognathic surgery and in identifying predictors influencing treatment decisions. A PRISMA-guided electronic database search (PubMed, Web of Science; 2009-2024; English/French) was performed to identify studies using machine learning (ML) or deep learning (DL) on cephalometric and clinical data. After screening and assessment for eligibility, 15 studies were critically appraised. Model performance was summarized using accuracy, sensitivity, specificity, and the area under the curve (AUC). ML algorithms (particularly Random Forest and XGBoost) and DL models (ResNet-based convolutional neural networks (CNNs)) achieved high accuracy for predicting surgical need. Frequently selected predictors included Wits appraisal, ANB angle, the maxillomandibular ratio (Mx/Md), overjet, and the divergence of the lower gonial angle. AI methods show promise for assisting treatment decisions in Class III malocclusion, with Random Forest and XGBoost performing well on tabular cephalometric data and CNNs on imaging. Larger, multicentre datasets and external validation are needed to improve reliability, address bias, and support clinical implementation.

Humans↗

PubMed2026

The future of precision oncology and artificial intelligence in Belgium: scenarios and policy responses.

PURPOSE: Precision medicine, also known as personalized medicine, enables the provision of tailored health services to patients. In the prevention, early detection, and treatment of cancers, precision medicine is highly promising, given the increasing use of genomic profiling for diagnosis and adapting therapies in several tumor types. Artificial Intelligence (AI) can support this process by analyzing vast amounts of relevant data. However, high-quality data and financial investments in the health system are essential for the implementation of precision medicine and AI solutions in routine cancer care. DESIGN/METHODOLOGY/APPROACH: Building on the quantitative outcomes of a foresight exercise published in another study, this article collects qualitative data to gain more detailed insights into the future of precision oncology in Belgium and discusses the role of AI in this field. It reports the results of a series of expert workshops, focusing on four hypothetical future scenarios that are centered around technological and economic issues that must be overcome for the widespread use of precision oncology in Belgium. FINDINGS: The study concludes that all four scenarios discussed in the workshops would require supportive policy measures in Belgium, which should go beyond mere technological and economic considerations, such as involving patient associations and the public in policy design or creating multi-disciplinary expert groups for precision medicine. ORIGINALITY/VALUE: To the best of our knowledge, this is the first study to employ foresight methodology to illustrate possible future scenarios, scrutinize feasible approaches for implementing precision oncology in Belgium, and discuss the use of AI in this context.

Belgium↗

PubMed2026

AI echo INSIGHT study: A prospective blinded randomized trial of artificial intelligence echocardiogram interpretation.

BACKGROUND: Transthoracic echocardiography (TTE) is the most commonly performed cardiac imaging modality with over 30 million studies annually. Demand for timely expert interpretation continues to outpace capacity, creating diagnostic delays and inter-observer variability that impact patient care. Recent research has suggested computer vision artificial intelligence (AI) models can generate accurate preliminary comprehensive TTE reports, however, prospective evaluation is needed to determine whether AI-assisted TTE interpretation can improve clinician efficiency while preserving diagnostic accuracy. METHODS: AI ECHO INSIGHT is a prospective randomized blinded clinical trial conducted at Kaiser Permanente Northern California that will evaluate 1200 historical TTE studies (1000 consecutive unselected studies plus 200 with moderate or greater valvular disease) interpreted using three workflows: (1) AI-generated preliminary report finalized by a blinded cardiologist (AI-assisted); (2) cardiologist-generated preliminary report finalized by a blinded cardiologist (cardiologist-assisted); and (3) sonographer-generated preliminary report finalized by a blinded cardiologist (sonographer-assisted). The primary outcome is the rate of substantial change between preliminary and final reports, comparing the AI-assisted workflow to the pooled cardiologist-assisted and sonographer-assisted workflows. Secondary outcomes include cardiologist interpretation time for report finalization, superiority testing for diagnostic accuracy, and reporting consistency. CONCLUSION: AI ECHO INSIGHT is a prospective randomized blinded clinical trial evaluating the clinical impact of AI-assisted TTE interpretation on diagnostic accuracy, cardiologist efficiency, and reporting consistency in real-world echocardiography workflows. TRIAL REGISTRATION: ClinicalTrials.gov registration number NCT07229300.

Humans↗

PubMed2026

Artificial Intelligence for Diagnosing Meibomian Gland Dysfunction: A Systematic Review and Meta-Analysis of Diagnostic Test Accuracy Studies.

PURPOSE: To identify, appraise, and synthesize the performance of artificial intelligence-based meibography reading as compared with human graders in diagnosing meibomian gland dysfunction. METHODS: We followed Cochrane methodology and reporting guidelines for diagnostic test accuracy reviews. To assess potential risk of bias and applicability, we used a modified Quality Assessment of Diagnostic Accuracy Studies-2 checklist. We applied bivariate logistic models to estimate summary sensitivity and specificity when appropriate and used the GRADE framework to rate the certainty of the evidence. RESULTS: We identified 14 eligible studies involving 5511 predominantly middle-aged participants (average age: 27-55 years) who were primarily female (≥54.5%). A total of 18,926 meibography images were obtained through noncontact infrared (11 studies) or in vivo confocal microscopy (three studies). Two studies reported external validation of deep learning models, 12 reported internally validated models, and one reported both. All but one study had high risk of bias in at least one domain; 12 studies raised high or intermediate concern about applicability. Based on three external evaluations, the summary sensitivity and specificity for diagnosing meibomian gland dysfunction from normal glands were 97.5% (95% confidence interval: 77.5%-99.8%) and 85.5% (95% confidence interval: 47.3%-97.5%). Sources of heterogeneity in internally validated models included study population, case mix, and others. The overall evidence was very low to low certainty because of imprecision, high risk of bias, and concerns about applicability. CONCLUSIONS: Artificial intelligence-based meibography grading appears less accurate than human graders. Future studies should adopt rigorous designs, including a more diverse participant pool (or image set), and external validation.

Humans↗

PubMed2026

Music therapy in artificial insemination by husband: Effects on psychological distress and clinical pregnancy rate.

BACKGROUND: Infertility treatment can be psychologically burdensome. Evidence on music listening during artificial insemination by husband (AIH) remains limited. We evaluated whether a standardized periprocedural music intervention reduced psychological distress and explored its association with clinical pregnancy. METHODS: In this single-center, open-label randomized trial, 254 women undergoing AIH were allocated 1:1 to routine care or routine care plus two 30-minute music-listening sessions, immediately before and after AIH. The prespecified primary outcome was the continuous 21-item Depression Anxiety Stress Scales (DASS-21) score at follow-up; clinical pregnancy was secondary. DASS-21 was completed at baseline and at day 35 after AIH. Clinical pregnancy was assessed by ultrasonography at days 30 to 35. RESULTS: Continuous DASS-21 anxiety, depression, and stress scores did not differ between groups after adjustment for baseline scores (all P ≥ .868). Absolute postintervention differences were 0.01 point or less for anxiety and depression and 0.08 point for stress, indicating neither statistical nor clinically meaningful improvement. Clinical pregnancy occurred in 34/127 participants (26.77%) in the intervention group and 14/127 (11.02%) in the control group (exploratory secondary outcome; P = .001). CONCLUSION: The intervention did not improve the primary psychological outcome. The higher clinical pregnancy rate is hypothesis-generating because the trial was not powered for pregnancy, the proposed psychological mechanism was not demonstrated, and nonspecific attention and other unmeasured factors cannot be excluded. Replication using an attention-matched control, immediate state-anxiety and physiological measures, participant-preference assessment, and live-birth follow-up is required.

Humans↗

PubMed2026

Second-Generation ELZA-sub400 Protocol: Individualized High-Fluence Cross-Linking for Ultra-Thin Keratoconus Corneas.

PURPOSE: To evaluate the safety and efficacy of a second-generation individualized corneal cross-linking (CXL) protocol (ELZA-sub400) using high-fluence UV-A irradiation in ultrathin ectatic corneas. DESIGN: Retrospective, single-center, consecutive interventional case series. METHODS: Twenty-nine eyes of 24 patients with progressive keratoconus or post-LASIK ectasia and a post-soak intraoperative thinnest stromal thickness <400 µm were included. After epithelial removal and riboflavin soaking, continuous UV-A irradiation (365 nm) at 3 or 9 mW/cm² was delivered with total fluence titrated up to 10 J/cm² based on intraoperative ultrasound pachymetry and a previously published nomogram targeting an uncross-linked stromal margin of approximately 70 µm above the endothelium. Outcomes were assessed at baseline and up to 12 months using corrected distance visual acuity (CDVA) and corneal parameters measured using Scheimpflug tomography and anterior segment OCT (AS-OCT) with Placido-based topography. The main outcome measure was the proportion of eyes without progression at 12 months, defined as <1.0 D increase in maximum keratometry (Kmax). Secondary outcomes included changes in CDVA, refraction, Kmax, stromal thickness, demarcation line depth, densitometry, and safety parameters. RESULTS: At 12 months, 22/29 eyes (76%; 95% CI, 57.9%-87.8%) met the nonprogression criterion. Mean change in Kmax was -0.77 ± 5.10 D (95% CI, -2.71 to 1.17; P = .418). Mean demarcation line-to-anterior stroma distance was 205 ± 64 µm (95% CI, 180.7-229.3), and demarcation line-to-endothelium distance was 64 µm (IQR, 49-152). All demarcation lines remained within the stromal layer; 15/29 eyes (51.7%) had a demarcation line located ≤70 µm from the endothelium. Median CDVA changed from 0.10 to 0.32 logMAR (P = .142). Minimum stromal thickness showed a median change of -4.0 µm (P = .309). No significant change was observed in densitometry, and no eye developed deep stromal haze or endothelial decompensation. CONCLUSIONS: Second-generation ELZA-sub400 CXL halted ectasia progression in 76% of ultrathin corneas at 12 months and was associated with an acceptable short-term safety profile, including stromal-confined demarcation line formation and no observed endothelial decompensation. The numerical decline in spectacle CDVA observed in this severely affected cohort did not reach statistical significance but is clinically important and warrants confirmation in larger prospective studies.

Humans↗

PubMed2026

Improving insurance deduction identification: a hybrid artificial intelligence model using machine learning and expert systems.

PURPOSE: Financial challenges in healthcare systems worldwide, especially in low- and middle-income countries like Iran, have increased hospitals' reliance on insurance reimbursements. Unrecognized insurance deductions often cause severe financial shortages, making efficient deduction management crucial. This study aimed to design a hybrid intelligent system for identifying and predicting insurance deductions by combining machine learning and expert system frameworks. DESIGN/METHODOLOGY/APPROACH: A mixed-methods design was applied in four stages. First, a scoping review identified the causes and patterns of insurance deductions. Second, interviews with 15 insurance experts produced a validated checklist and a dataset from inpatient billing records. Third, using the CRISP-DM methodology, machine learning algorithms were developed and tested in SPSS Modeler alongside a fuzzy expert system developed in MATLAB. Finally, the model was validated using the holdout method. FINDINGS: Four categories of deduction drivers were identified: service provision, registration errors, document submission issues, and revenue conversion processes. The CHAID decision tree outperformed other algorithms with a 99% precision rate and the lowest Mean Absolute Error (9.43). A brief assessment of potential overfitting was conducted to ensure that the CHAID model's high accuracy was interpreted cautiously and supported by the validation results. The fuzzy expert system with validated rules was adaptable for deduction classification, especially for cases unsuitable for quantitative modeling. ORIGINALITY/VALUE: The hybrid model improves detection and prevention of deductions, offering actionable insights for hospital administrators, insurers, and policymakers. Its implementation can enhance hospital information systems, streamline claims processing, and optimize revenue management amid financial constraints.

Machine Learning↗

PubMed2026

Artificial intelligence for anticancer drug discovery from natural products of macroalgae and sponges: A systematic review.

Marine natural products (MNPs) from macroalgae and marine sponges have inspired clinically important anticancer agents, including the cytarabine pharmacophore and the eribulin scaffold, while cyanobacterial dolastatin chemistry supplies the auristatin payloads of several marine-inspired antibody-drug conjugates (ADCs) such as brentuximab vedotin. Artificial intelligence (AI) methods, encompassing both classical machine learning (ML) with hand-engineered features and modern deep learning (DL) with many-layered neural networks, are increasingly supporting key decisions in natural-product anticancer drug discovery, including bioactivity prediction, target identification, absorption, distribution, metabolism, excretion and toxicity (ADMET) filtering, generative analogue design, and the selection of preclinical candidates. DL architectures relevant to this field include graph neural networks, transformer-based molecular generators, diffusion models for protein-ligand docking, and convolutional networks for mass spectrometry, while classical ML contributes interpretable fingerprint-based bioactivity models and molecular networking for dereplication. This review follows a systematic literature review methodology to organize the landscape of AI methods now applied to MNP anticancer discovery, distinguishing ML and DL approaches where relevant, situating them within the chemical context of macroalgal and sponge-derived oncology leads, and critically examining published case studies, including validation level (computational, in vitro, in vivo, clinical). The principal bottleneck for medical translation has shifted partly from algorithmic capability toward data infrastructure and experimental validation. Sparse, heterogeneous, and taxonomically biased bioactivity records limit what current models can learn and reduce the reliability of AI-prioritized candidates entering the preclinical pipeline. A roadmap is proposed that prioritizes open MNP-specific benchmarks, symbiont-aware modeling, and active learning loops with synthesizability and ADMET constraints. These AI workflows may accelerate the prioritization of marine-derived anticancer leads and support earlier, more evidence-based translational decisions in oncology drug development.

Biological Products↗