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PubMed2026

Diagnostic accuracy of bronchoalveolar lavage fluid-based testing for pulmonary cryptococcosis: A systematic review and meta-analysis.

BACKGROUND: Pulmonary cryptococcosis(PC) presents diagnostic challenges because of its non-specific clinical and radiological manifestations. Bronchoalveolar lavage fluid (BALF)-based testing, which includes latex agglutination (LA) and lateral flow assay (LFA), offers a minimally invasive diagnostic method, yet its pooled diagnostic accuracy remains unclear. METHODS: We systematically searched PubMed, Embase, Cochrane Library, and Scopus from inception to May 2026. Studies evaluating BALF-based testing for PC with extractable 2 × 2 data were included. The methodological quality of relevant studies was assessed by the QUADAS-2 tool. Pooled sensitivity, specificity, likelihood ratios, and diagnostic odds ratio (DOR) were estimated using a bivariate random-effects model. Subgroup analyses were performed by testing method and reference standard type. Heterogeneity was evaluated through paired forest plots, HSROC visualization, and exploratory bivariate meta-regression. RESULTS: The pooled sensitivity was 0.87 (95% CI: 0.81-0.91), and the specificity was 0.99 (95% CI: 0.982 - 0.995). The pooled positive likelihood ratio (PLR) was 88.00 (95% CI: 47.39 - 163.42), the negative likelihood ratio (NLR) was 0.13 (95% CI: 0.09 -0.20), and the DOR was 658.50 (95% CI: 285.36-1519.55). No significant threshold effect or publication bias was detected. Exploratory meta-regression suggested a possible assay-method effect in the joint model (P = 0.03), mainly driven by specificity (P = 0.01). CONCLUSIONS: The study demonstrates the high accuracy of CrAg in BALF for the diagnosis of pulmonary cryptococcosis, supporting its role as an important adjunctive diagnostic tool, particularly when tissue biopsy is not feasible or rapid results are needed. Larger prospective studies with standardized protocols are needed to validate these estimates.

Humans↗

PubMed2026

Factors influencing the enhancement of the new iron triangle in healthcare organisations.

PURPOSE: A new paradigm, "healthcare's new iron triangle," has been developed to emphasise the technological perspective of healthcare delivery, focusing on automation, value and empathy. The study aims to build a conceptual model and to identify factors for the enhancement of the new iron triangle in healthcare organisations. DESIGN/METHODOLOGY/APPROACH: The healthcare organisation is the primary focus point of the current study. To determine the factors, a survey of the literature and healthcare experts' opinions was conducted. The healthcare professionals validated the identified factors. Data for this study were gathered using a closed-ended questionnaire and scheduled interviews. The study employed "Total Interpretive Structural Modeling methodology and Matriced' Impacts Croise´s Multiplication Appliqué´ a UN Classement/Cross-Impact Matrix Multiplication Applied to a Classification (MICMAC) analysis" to address the "why" and "how" the factors interact and prioritise the identified factors. FINDINGS: The study found that organisational structure (F8), artificial intelligence (F1), innovation (F2) and human resources (F5) are the driving or key factors of the study. RESEARCH LIMITATIONS/IMPLICATIONS: The study primarily focused on identifying factors for the enhancement of a new iron triangle in healthcare organisations. The scope could eventually be expanded to explore more areas. PRACTICAL IMPLICATIONS: Academics and other stakeholders will have a better understanding of the key drivers for the enhancement of the new iron triangle in healthcare organisations. ORIGINALITY/VALUE: In this study, total interpretive structural modeling and cross-impact MICMAC analysis are proposed as an innovative approach to address the new iron triangle in healthcare organisations.

Humans↗

PubMed2026

Characterizing 46,XY Differences of Sex Development: Novel Genetic Variants and Diagnostic Pitfalls in a Single-Center Cohort.

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17β-hydroxysteroid dehydrogenase deficiency↗

PubMed2026

Chronic neurological diseases with acute respiratory failure in a real-life cohort: insights into ICU and long-term survival-A retrospective study.

BACKGROUND: Patients with chronic neurological diseases (CND) are at increased risk of pulmonary complications that often require ICU admission. This study aimed to identify clinical factors associated with ICU mortality and long-term survival in patients with CND who developed acute respiratory failure (ARF). METHODS: This retrospective cohort study was conducted in a level III respiratory ICU. Patients with pre-existing CND admitted to the ICU with ARF were included. ICU mortality was analyzed using multivariable logistic regression. Long-term survival after ICU discharge was evaluated using Kaplan-Meier survival analysis and Cox proportional hazards models. Mortality timing was further characterized using hazard function analysis. RESULTS: A total of 220 patients were included; the most common neurological diagnoses were dementia (37.3%), stroke (22.7%), and amyotrophic lateral sclerosis (14.1%). ICU mortality was 33.6%. Higher APACHE II scores were independently associated with increased ICU mortality (OR 1.076 per point increase; 95% CI 1.029-1.126; p < 0.001). Long-term survival differed significantly by post-discharge respiratory support strategy, with Kaplan-Meier analysis demonstrating more favorable survival patterns among patients receiving home non-invasive mechanical ventilation (NIMV) (p = 0.003). In Cox regression analysis, age, home NIMV, and feeding modality at discharge were independently associated with long-term outcomes. Survival analyses revealed an early clustering of deaths within the first months after ICU discharge, particularly among patients with dementia. CONCLUSIONS: In patients with CND, acute physiological severity was the main determinant of ICU mortality, whereas long-term survival after ICU discharge was poor, with deaths clustering within the first months thereafter. Post-discharge respiratory support and nutritional management should be individualized according to the expected clinical trajectory and patient values.

Humans↗

PubMed2026

Diagnostic accuracy of nuclear STAT6 immunohistochemistry for solitary fibrous tumour: a systematic review and meta-analysis.

Nuclear STAT6 immunohistochemistry is the diagnostic surrogate for the NAB2::STAT6 fusion of solitary fibrous tumour (SFT); its sensitivity is established, but specificity varies for unexamined reasons. This review quantified pooled accuracy and tested whether antibody clone and nuclear threshold govern specificity. PubMed, Scopus and Web of Science were searched to 29 June 2026 for studies reporting nuclear STAT6 immunohistochemistry against a reference standard (NAB2::STAT6 confirmation and/or expert consensus) in SFT and comparators, with extractable two-by-two data. Two reviewers screened, extracted data and applied QUADAS-2. A bivariate generalised linear mixed model gave summary sensitivity and specificity, and exploratory subgroup analysis and meta-regression tested antibody clone, anatomical site and reference-standard type. Twenty-three studies (1216 SFT and 4715 comparators) were included. Summary sensitivity was 98.7% (95% confidence interval 96.7-99.5) and specificity 99.1% (97.8-99.6); the diagnostic odds ratio was approximately 8656. The monoclonal YE361 subgroup (8 studies) reached specificity 99.9% (99.3-100), with one false positive among 861 comparators, versus 98.1% (96.0-99.1) for polyclonal and other antibodies. False positives concentrated in dedifferentiated liposarcoma and prostatic stromal tumours. Estimates were stable after removing studies at higher risk of bias (98.9%/99.1%) and on leave-one-out analysis; the Deeks test was non-significant (p = 0.08). Nuclear STAT6 immunohistochemistry is therefore highly sensitive and specific for SFT, and the residual specificity loss is structured and largely avoidable: the monoclonal YE361 read at a strict nuclear threshold is preferred, with MDM2 and CDK4 applied to exclude dedifferentiated liposarcoma when nuclear STAT6 is unexpectedly positive.

Humans↗

PubMed2026

Non-motor symptoms and healthcare utilization before diagnosis of myasthenia gravis: a nationwide cohort study.

BACKGROUND: Non-motor symptoms have been reported prior to myasthenia gravis (MG) diagnosis. However, the temporal patterns of non-motor symptoms and healthcare utilization before MG diagnosis remain unclear. METHODS: We conducted a retrospective, population-based cohort study using the Korean National Health Insurance Service (KNHIS) database from 2011 to 2021. Incident MG cases were identified using the International Classification of Diseases, Tenth and Rare Intractable Disease codes. Individuals younger than 20  years or with missing health screening data were excluded. Each MG case was matched 1:10 by age, sex, and index date to controls. Non-motor symptoms and healthcare utilization were defined using operational criteria derived from KNHIS claims data. Rate ratios (RRs) and 95 % confidence intervals (CIs) were estimated across four prespecified intervals (0-1, 1-2, 2-5, and 5-10  years) before MG diagnosis. RESULTS: We included 8,355 MG patients and 83,550 controls (mean age, 53.7  years; male, 44 %). MG patients had higher rates of any non-motor symptoms over 10  years(RR 1.34; 95 % CI 1.30-1.39), with the sharpest increase in the year before diagnosis. Depression, anxiety, migraine, constipation, and insomnia consistently showed higher RRs across all intervals. Hospitalizations (RR 1.66; 95 % CI 1.61-1.71) and outpatient clinic visits (RR 1.10; 95 % CI 1.04-1.17) were consistently higher across 10  years, peaking during the 0-1 year before MG diagnosis. CONCLUSION: Non-motor symptoms and healthcare utilization increased years before MG diagnosis. Earlier recognition of these symptom patterns may facilitate timelier evaluation for MG and improve diagnostic pathways.

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

Diagnostic criteria and severity assessment for syndesmosis injury using magnetic resonance imaging: A systematic review.

High ankle sprains involving syndesmosis injury present challenges in both diagnosis and severity assessment. Magnetic resonance imaging is widely regarded as the preferred modality for evaluating syndesmosis injury and related structural damage. This systematic review primarily examined the diagnostic utility of magnetic resonance imaging. Secondarily, it explores grading and prognostics of syndesmosis injuries with magnetic resonance imaging and identified possible imaging parameters predictive of injury severity. A comprehensive search of MEDLINE, Embase, CINAHL Complete, and Scopus was performed through February 12, 2025, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Peer-reviewed human studies in English that used magnetic resonance imaging to assess syndesmosis injury were included. Excluded were review articles, case reports, abstract-only studies, and biomechanical or cadaveric investigations. Twenty-seven studies comprising 1931 ankles met inclusion criteria. Magnetic resonance imaging demonstrated high diagnostic accuracy for complete tears of the anterior and posterior inferior tibiofibular ligaments. Ancillary signs such as the ring-of-fire edema pattern, distal tibiofibular joint effusion, and widening of the distal joint space exhibited high specificity with variable sensitivity and may assist in grading injury severity. Magnetic resonance imaging in chronic syndesmosis injury primarily detects fibrotic scarring and post-injury changes. Evidence gaps remain regarding the parameters that best determine injury severity and indicate early surgical intervention in competitive athletes. Consolidating multiple magnetic resonance imaging findings into standardized diagnostic criteria may improve reliability and clinical decision-making.

Humans↗

PubMed2026

Acute Haemodynamic and Perceptual Responses to Graded Intradialytic Exercise in Patients on Maintenance Haemodialysis: A Randomised Crossover Trial.

BACKGROUND: Acute responses to graded intradialytic exercise in people receiving haemodialysis remain incompletely characterised. OBJECTIVES: To examine acute haemodynamic and perceptual responses to graded intradialytic resistance exercise compared with a non-exercise control condition. DESIGN: Randomised crossover trial. PARTICIPANTS: Forty-eight clinically stable adults receiving maintenance haemodialysis. MEASUREMENTS: Participants completed seated control and graded lower-limb resistance exercise targeting Borg category-ratio 10 ratings of 3, 5 and 7. Systolic and diastolic blood pressure, mean arterial pressure, heart rate, peripheral oxygen saturation, rating of perceived exertion and acute fatigue were measured before, immediately after and 30 min after each condition. RESULTS: Responses increased progressively with perceived intensity. Compared with control, higher perceived intensity increased systolic blood pressure by 14.9 mmHg (95% confidence interval = 12.7-17.0), diastolic blood pressure by 8.4 mmHg (6.8-10.0), mean arterial pressure by 10.6 mmHg (9.2-12.0) and heart rate by 15.5 beats per minute (12.9-18.1). Rating of perceived exertion increased by 6.7 points (95% confidence interval = 6.3-7.1). Mean peripheral oxygen saturation remained between 95.1% and 97.5%, with no value below 90%. Recorded symptoms occurred in 13 out of 48 higher perceived-intensity sessions; no serious adverse events occurred. CONCLUSIONS: Graded, rating-guided intradialytic resistance exercise produced clear acute dose-response haemodynamic and perceptual changes. These findings support supervised individualisation of acute exercise dose but do not establish long-term safety or superiority of higher perceived-intensity training. TRIAL REGISTRATION: Pan African Clinical Trial Registry: PACTR202606476360900.

Humans↗

PubMed2026

Future promise, current clinical ambiguity: a systematic review of machine learning algorithm outputs predicting risk of cardiovascular disease.

OBJECTIVE: To examine whether the outputs of machine learning algorithms designed to predict risk of cardiovascular disease (CVD) address known deficiencies of the Framingham Risk Score (FRS) and improve risk estimates. METHODS: For this critical review, Medline, Embase and IEEE were searched from inception to 1 January 2025. Included were studies describing machine learning algorithms designed to specifically compare output of cardiovascular risk assessment with the FRS. Commentaries, letters, unpublished work or non-peer-reviewed papers were excluded.Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, two reviewers screened titles and abstracts independently, then populated a purpose-built data extraction form. A subsequent qualitative thematic analysis focused on algorithms' strengths, added value, potential harms, unintended consequences and equity implications.The main outcome assessed was whether, among healthy adults, the algorithm improved CVD risk prediction relative to the FRS. RESULTS: Of 707 studies retrieved, 29 met inclusion criteria. 23 reported improved predictive ability relative to the FRS. Most datasets and/or medical records used included sociodemographic predictors of CVD not included among FRS inputs. Some added costly diagnostic tests like CT angiography to FRS screening indicators. When they were defined, inputs and outcomes such as hypertension or myocardial infarction did not always adhere to FRS values. Statistical significance was generally taken as a proxy for clinical significance. Some algorithms overestimated the number at risk compared with the FRS without discussing whether that larger proportion might be at risk of overdiagnosis rather than CVD, while a few decreased the proportion found to be at risk. CONCLUSIONS: Use of artificial intelligence to improve accuracy of risk assessment for CVD demonstrates the technological capacity to merge known sociodemographic predictors with biologic variables and examine non-linear interactions among these. Still needed to achieve patient benefit is clinical insight, adherence to screening principles and cost-benefit assessment of inputs selected.

Humans↗

PubMed2026

Polygenic risk scores in major depressive disorder: A systematic review across diagnostic, treatment, course/severity, and subtype domains.

BACKGROUND: Major depressive disorder (MDD) is heterogeneous across diagnostic, treatment-related, course/severity, and subtype domains. Polygenic risk score (PRS) studies have examined these domains, but differences in PRS sources, samples, methods, and endpoint definitions have fragmented the evidence. We synthesised findings and examined potential contributors to heterogeneity. METHODS: PubMed/MEDLINE, Embase, PsycINFO, and Web of Science were searched for studies published from January 2016 through 25 November 2025. Result records were synthesised using SWiM, and certainty was assessed with an adapted GRADE framework. RESULTS: Sixty studies contributed 493 retained records; 450 were descriptively classified as positive, null, or reverse, although records were not independent. Positive findings accounted for 44/56 diagnostic, 61/273 treatment-related, 64/100 course/severity, and 14/21 subtype records. For MDD/depression-derived PRSs and case-control MDD status, all 10 contributing studies showed higher liability in cases (exploratory exact sign test p = 0.002; FDR q = 0.004). The same PRS group showed positive findings for overall depressive symptom severity (14/18), although the study-level test was imprecise (5/5 studies; p = 0.063). Pharmacological response/remission findings for these PRSs were mostly null or directionally mixed (10 positive, 18 null, and 9 reverse). Treatment-resistant depression (TRD) findings differed by operational definition. Atypical and psychotic subtype signals arose mainly from single-study PRS and endpoint contrasts. CONCLUSIONS: PRS evidence was clearest for MDD diagnostic status and showed a tentative pattern for overall symptom burden. Treatment and subtype findings were less consistent or less replicated. Larger, ancestrally diverse studies with standardised endpoints and transparent PRS methods are needed.

Humans↗

PubMed2026

Emerging hantavirus risks in mass gatherings: epidemiology, diagnostic challenges, and outbreak preparedness.

Hantaviruses are emerging rodent borne zoonotic pathogens of increasing global public health concern because of their high mortality, expanding ecological distribution, and potential for international dissemination. Although traditionally associated with sporadic rural outbreaks, recent ecological disruption, climate variability, urbanization, and increased global mobility have heightened concerns regarding hantavirus risks in mass gathering settings. This review critically examines the epidemiology, transmission uncertainty, diagnostic and surveillance challenges, and preparedness strategies related to hantavirus infections in the context of mass gatherings, including religious events, refugee settlements, cruise tourism, sporting events, and temporary accommodations. Particular emphasis is placed on the 2026 multinational cruise ship associated outbreak linked to the MV Hondius, which highlighted vulnerabilities related to delayed diagnosis, international passenger dispersal, and uncertainties surrounding possible human to human transmission of Andes virus. Current evidence indicates that hantavirus transmission occurs primarily through inhalation of aerosolized rodent excreta; however, controversies regarding limited interpersonal transmission, environmental persistence, and asymptomatic infections continue to complicate risk assessment and outbreak preparedness. Diagnostic limitations, underreporting, insufficient environmental surveillance, and lack of mass gathering specific preparedness frameworks remain major public health challenges, especially in resource limited settings. Strengthening proactive preparedness through integrated One Health approaches, ecological surveillance, genomic monitoring, AI driven epidemic intelligence, and coordinated international response systems is essential for mitigating future risks. The review emphasizes the urgent need for multidisciplinary research and evidence based policy development to improve global preparedness against emerging hantavirus associated threats in increasingly interconnected mass gathering environments.

Humans↗

PubMed2026

Diagnostic performance of machine learning models for malignant and non-malignant pleural effusion: Systematic review and meta-analysis.

BACKGROUND: Accurately distinguishing malignant pleural effusion (MPE) from non-malignant pleural effusion is clinically important, but the generalisability and methodological quality of machine-learning (ML) models remain uncertain. METHODS: We searched eight databases to 23 April 2026. Diagnostic performance was pooled using random-effects and Reitsma bivariate models, and study quality was assessed using PROBAST+AI. RESULTS: Forty-two studies were included; 17 contributed to the AUC meta-analysis and 14 to the bivariate analysis. The pooled AUC was 0.90 (95 % CI 0.85-0.94; 95 % prediction interval 0.62-0.98), with sensitivity of 0.80 (95 % CI 0.77-0.83) and specificity of 0.87 (95 % CI 0.79-0.92). Only nine studies reported external, temporal or independent validation. Externally validated studies had a lower pooled AUC than studies without external validation (0.83 vs 0.92), with lower specificity observed in the two externally validated studies contributing sensitivity and specificity data. All 42 development assessments had high overall quality concerns, and all 42 model evaluations were judged at high risk of bias. CONCLUSIONS: ML models showed good apparent accuracy for distinguishing MPE from non-MPE, but the evidence was limited by substantial heterogeneity, high risk of bias and scarce external validation. The pooled estimates reflect the average performance of different selected models rather than the expected accuracy of a single clinical test. ML models should be regarded as adjuncts to existing diagnostic pathways until they are confirmed by rigorous multicentre prospective external validation and clinical-impact studies.

Humans↗

PubMed2026

Effectiveness of Yoga and Combined Exercise in Female With Rheumatoid Arthritis: Randomized Controlled Trial.

BACKGROUND: Although exercise is beneficial for Rheumatoid Arthritis (RA), the comparative efficacy of different modalities for patients in clinical remission remains unclear. This study compared the short- and long-term effects of yoga versus a combined exercise programme on pain, balance, mobility, fatigue, depression, and quality of life in females with RA in remission. METHODS: In this single-blind, randomized controlled trial, 74 female participants were allocated to yoga (n = 25), combined exercise (n = 25), or a usual care control group (n = 24). The intervention groups underwent an 8-week supervised programme. Clinical assessments, including the Visual Analogue Scale (pain), Berg Balance Scale, Timed Up and Go Test, Beck Depression Inventory, Fatigue Severity Scale, and Short Form-36, were conducted at baseline, post-intervention (8 weeks), and follow-up (20 weeks). RESULTS: Both intervention groups demonstrated significant improvements in all outcome measures compared with the control group at post-treatment and follow-up (p < 0.05). Notably, the yoga group exhibited superior outcomes compared to the combined exercise group in reducing pain intensity (median reduction of 4.00 vs. 2.00 points; p < 0.001, η2 = 0.724), as well as in physical function, balance, fatigue, depression, and quality of life at the 20-week follow-up. These benefits may be partly attributed to the incorporation of breathing and relaxation techniques inherent to yoga practice. CONCLUSIONS: Both 8-week yoga and combined exercise programs are effective in managing residual symptoms in females with RA in clinical remission. However, yoga appears to provide superior benefits in pain management and psychosocial well-being, supporting its integration into multidisciplinary RA management protocols, particularly for addressing psychosocial burden in patients achieving remission. TRIAL REGISTRATION: This study was retrospectively registered at NCT07072754 (clinicaltrials.gov).

Humans↗

PubMed2026

Repeated scoring with the adult appendicitis score improves the sensitivity and the specificity of appendicitis diagnosis in patients with early equivocal signs of appendicitis: a secondary analysis.

PURPOSE: The utilization of computed tomography in the early stage of acute appendicitis may result in overdiagnosis and unnecessarily expose patients to ionising radiation. The Adult Appendicitis Score (AAS) can be used to select patients for imaging. Observation and re-scoring in the DIAMOND trial reduced the need for imaging. Now, we wanted to determine if the change in AAS (∆AAS) can serve as a diagnostic tool to select patients for imaging even more precisely. METHODS: Eighty-eight patients with early equivocal appendicitis participated in the observation arm of the DIAMOND trial. The data for these patients were reanalysed, and ∆AAS during the observation was calculated. The baseline AAS, final AAS, and the change in C-reactive protein (∆CRP) were selected as reference standards. RESULTS: Eighty-three patients with complete data were included in the analysis. The AUROC (Area Under the Receiver Operating Characteristic) values are as follows: ∆AAS, 0.932 (95% CI 0.868-0.996); baseline AAS, 0.629 (95% CI 0.498-0.760); final AAS, 0.936 (95% CI 0.886-0.987); and ∆CRP, 0.796 (95% CI 0.696-0.897). Using receiver operating characteristic curves, we established the thresholds for low (AAS ≤ -2), intermediate (AAS -1 to 0), and high (AAS ≥ 1) probability of appendicitis. The negative predictive value for the low-probability group and the positive predictive value for the high-probability group concerning acute appendicitis were 97% and 94%, respectively. CONCLUSION: Patients with equivocal signs of appendicitis may benefit from short observation and the calculation of ∆AAS to reduce overdiagnosis and exposure to excessive imaging. REGISTRATION: The DIAMOND trial was officially registered on ClinicalTrials.gov (NCT02742402) on April 13, 2016.

Adult↗

PubMed2026

Emerging techniques of CRISPR/Cas system in antiviral therapy and diagnostics: Applications, limitations, and translational perspectives.

The CRISPR/Cas (clustered regularly interspaced short palindromic repeats) system is a versatile technology for developing antiviral medicines and editing viral genomes in both diagnostics and vaccine synthesis. Emerging insights into class 2 effectors, such as Cas9, Cas12, and Cas13, which target viral DNA and RNA, have revolutionized vaccines against viruses such as HIV, HPV, HBV, and EBV. Innovative diagnostic techniques such as SHERLOCK, DETECTR, and FELUDA have demonstrated system's diversity and accuracy in detecting the virus markers, supporting clinical decision-making, indicating adaptability and precision of CRISPR. This review critically evaluates CRISPR's role in RNA editing, emphasizing its importance for functional genomics and development of recombinant vaccines. Translational challenges are critically discussed, including off-target effects, delivery limitations, and ethical issues, for which unique approaches such as high-fidelity Cas variants, non-viral delivery systems, and bioethical frameworks are evaluated to address these limitations. This review also covers other social implications, such as accessibility and biosecurity risks, associated with CRISPR technologies Collectively, these advances underscore the transformative potential of CRISPR technologies in shaping next-generation antiviral diagnostics and therapeutics.

CRISPR-Cas Systems↗

PubMed2026

Macroprolactinemia as a diagnostic pitfall in hyperprolactinemia: a systematic review and quantitative synthesis.

CONTEXT: Macroprolactinemia is a well-recognized cause of hyperprolactinemia and an important diagnostic pitfall in endocrine practice. However, interpretation of published quantitative prolactin data remains sparse as studies vary in confirmation method, assay platform, polyethylene glycol (PEG) recovery cutoff, and reporting of prolactin measurement. EVIDENCE ACQUISITION: PubMed, Embase, Scopus, Web of Science, the Cochrane Library, and Google Scholar were systematically searched. Eligible studies reported macroprolactinemia-specific quantitative prolactin data in patients with confirmed macroprolactinemia defined by PEG precipitation, gel filtration chromatography (GFC), or both. Two reviewers independently performed study selection, data extraction, and quality assessment. Findings were summarized using study-level descriptive synthesis. The review was prospectively registered in PROSPERO and conducted in accordance with PRISMA 2020 guidelines. EVIDENCE SYNTHESIS: Forty-five studies encompassing 2853 macroprolactinemia cases from 21 413 screened patients with hyperprolactinemia across 22 countries were included. Among 33 studies eligible for primary quantitative analysis, the median study-level central total prolactin attributed to macroprolactinemia was 61.4 ng/mL ([IQR] 42.0-80.0; range 28.1-137.6), and the median study-level post-PEG monomeric prolactin was 11.7 ng/mL (IQR 8.3-13.2; range 4.0-17.0)). The median study-level maximum total prolactin was 264.5 ng/mL (IQR 97.0-425.5; range 81.8-663.0); extreme elevations were attributable to coexisting prolactinomas. CONCLUSION: In confirmed macroprolactinemia, total prolactin elevation is typically moderate, and post-PEG monomeric prolactin is usually within or near the normal range. The post-PEG monomeric prolactin value, rather than percent recovery alone, is the most informative parameter for distinguishing isolated macroprolactinemia from coexisting true hyperprolactinemia. These quantitative benchmarks may help clinicians to avoid unnecessary investigation or treatment.

Humans↗

PubMed2026

Diagnostic Performance of Machine Learning for Systemic Lupus Erythematosus: Systematic Review and Meta-Analysis.

BACKGROUND: Early and accurate diagnosis of systemic lupus erythematosus (SLE) and its organ involvement is essential. Previous reviews of machine learning (ML) in SLE combined heterogeneous tasks and validation strategies and may have overinterpreted model performance. OBJECTIVE: This study evaluated the diagnostic performance of ML and deep learning (DL) models for 3 clinically distinct SLE-related tasks: SLE classification or diagnosis, lupus nephritis (LN) diagnosis, and neuropsychiatric systemic lupus erythematosus (NPSLE) discrimination. We also assessed methodological quality and certainty of evidence. METHODS: PubMed, Embase, Cochrane Library, Web of Science, and IEEE Xplore were searched from January 2014 to April 2026. Eligible peer-reviewed diagnostic accuracy studies developed or validated ML or DL models for 1 of the 3 prespecified tasks, used an accepted reference standard, and provided data for a 2×2 contingency table. Bivariate random-effects meta-analyses with the Hartung-Knapp-Sidik-Jonkman adjustment were used to pool sensitivity and specificity. We reported 95% prediction intervals (PIs), assessed risk of bias using the Quality Assessment of Diagnostic Accuracy Studies for Artificial Intelligence tool (QUADAS-AI; Viknesh Sounderajah [Imperial College London]), and evaluated certainty of evidence using the Grading of Recommendations Assessment, Development, and Evaluation framework for diagnostic test accuracy. RESULTS: Twenty-nine studies were included: 17 for SLE classification, 5 for LN diagnosis, and 7 for NPSLE discrimination. In the primary task-stratified analysis, pooled sensitivity was 0.91 (95% CI 0.86-0.94; 95% PI 0.56-0.99), and pooled specificity was 0.94 (95% CI 0.91-0.96; 95% PI 0.69-0.99), with low heterogeneity (I²=23.9% and 22.9%, respectively). DL models showed a sensitivity of 0.93 and specificity of 0.95, compared with 0.88 and 0.94 for traditional ML models. Certainty of evidence was high for most analyses but low for LN diagnosis because of inconsistency and imprecision. All studies were retrospective, and only 9 of 29 (31%) performed independent external validation. Overall risk of bias was high or unclear in 22 of 29 (75.9%) studies. No study reported model calibration, decision-curve analysis, or net clinical benefit. CONCLUSIONS: ML models showed promising diagnostic accuracy across 3 distinct SLE-related tasks, but wide PIs, limited external validation, and pervasive risk of bias restrict conclusions about real-world generalizability. Prospective multicenter studies with standardized tasks and reference standards, independent external validation, and formal assessment of calibration and clinical utility are required before clinical implementation.

Humans↗