SEARCH · Search PubMed

Results for “Insemination, Artificial”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

51 records · Page 2Linked to original sources

PubMed2026

AI-driven snapshot hyperspectral imaging for on-line sorting systems in food industry: From real-time sensing to intelligent decision-making.

High-throughput food sorting requires rapid, non-destructive detection of external defects, foreign materials, and internal quality attributes in heterogeneous food matrices. Conventional scanning hyperspectral imaging may suffer from motion-induced spatial-spectral mismatches, whereas snapshot hyperspectral imaging (S-HSI) captures spectral images within a single integration time. However, its advantage is limited by trade-offs in resolution, signal-to-noise ratio (SNR), reconstruction uncertainty, and calibration stability, which are further amplified by variable tissue structure, surface reflection, moisture, and fat distribution in foods. This review critically examines artificial intelligence (AI)-driven S-HSI for on-line food sorting within a sensing-representation-decision-execution framework. Compact architectures are compared according to their physical constraints, food-sorting suitability, and ability to support mapping between spectral responses and physicochemical quality attributes. AI strategies are reviewed for spectral reconstruction, image restoration, spatial-spectral representation, band selection, uncertainty-aware decision-making, and edge implementation. AI can partially compensate for snapshot-specific limitations, but current evidence remains largely limited to laboratory or prototype studies. Future work should link system performance to food safety and quality outcomes by reporting throughput, decision latency, calibration drift, missed-detection risk, false-rejection cost, and closed-loop sorting success.

Hyperspectral Imaging↗

PubMed2026

Artificial intelligence enabled social robotic interventions (PARO) in Australian dementia care: A systematic review and meta-analysis.

BACKGROUND: Although there is a growing body of research indicating that Personal Robot/Social Robot could be used in various aspects of care for individuals with dementia, little is known about how well these types of interventions work in an actual hospital setting in Australia. AIMS & OBJECTIVES: The objective of the present systematic review and meta-analysis is to assess the effectiveness of PARO-based socially assistive robotic intervention in terms of its effectiveness outcomes towards the reduction of dementia-related behavioural and psychological symptoms in Australian based healthcare settings. METHODS: A systematic search was conducted across five electronic databases, including MEDLINE (PubMed), EMBASE, CINAHL, PsycINFO, and the Cochrane Library, to identify randomised controlled trials (RCTs) investigating PARO-based socially assistive robotic interventions for dementia in Australian healthcare settings. This review was registered with PROSPERO (CRD420251251916) and followed the PRISMA 2020 guidelines. In addition, the Cochrane Risk of Bias tool (RoB 2) was used to evaluate the risk of bias across all studies. Pooled standardised mean differences (SMD) with 95 % confidence intervals (CI) were calculated for agitation, anxiety, and depression. Heterogeneity across studies was evaluated using the I2 statistic. RESULTS: Six RCTs involving 1444 participants were identified for inclusion in this review. AI-enabled socially assistive robotic interventions, specifically the PARO therapeutic robot, significantly reduced agitation and anxiety when compared to standard treatment or control conditions. The pooled analysis showed that agitation [SMD = -0.44 (95 % CI: -0.70, -0.18) p = 0.0008] and anxiety [SMD = -0.59 (95 % CI: -0.91, -0.27) p = 0.0003] were reduced significantly, while the decrease in depression [SMD = -0.44 (95 % CI: -0.95, -0.07) p = 0.09] scores was non-significant among dementia patients receiving PARO-based socially assistive robotic interventions as compared to the control. The overall risk of bias across all six studies was considered low to moderate. CONCLUSION: PARO-based socially assistive robotic interventions may provide preliminary evidence of effectiveness in reducing agitation and anxiety in individuals with dementia in Australian healthcare, but the evidence regarding the reduction of depression remains unclear. Therefore, additional high-quality trials with consistent methodology and extended follow-up will be necessary to determine both the short-term and long-term clinical efficacy and practicality of implementing these interventions into practice.

Humans↗

PubMed2026

Applications of quantum AI in brain disorder diagnosis: A systematic review.

BACKGROUND AND OBJECTIVE: Brain disorder diagnosis and prediction remain challenging because neuroimaging, electrophysiological, behavioral, and multimodal data are high-dimensional, noisy, heterogeneous, and limited by small clinical cohorts. This systematic review synthesised applications of quantum artificial intelligence (QAI) for brain disorder diagnosis, prediction, detection, and monitoring. METHODS: Following PRISMA guidelines, studies published from 2016 to 13 January 2026 were retrieved from Scopus, Web of Science, and IEEE Xplore. After screening, 36 studies met the eligibility criteria and were qualitatively analysed according to disorder category, data modality, QAI method, implementation setting, validation strategy, and performance. RESULTS: At the broader disease-group level, neurodegenerative disorders were the most frequently investigated, followed by mental health and psychiatric disorders. At the individual level, Parkinson's disease and schizophrenia were the leading applications, followed by depression, anxiety, Alzheimer's disease, and stress-related tasks. MRI-based modalities were the most frequently used data source, followed by multimodal data and EEG. Methodologically, primary QAI approaches were dominated by quantum neural and QDL architectures, followed by quantum-inspired optimization or feature-selection methods and quantum-kernel/conventional QML classifiers. Qiskit/IBM Quantum and PennyLane were the most frequently reported quantum software frameworks. However, most studies relied on simulators, classical quantum-inspired implementations, or unclear implementation settings, with limited real-hardware evaluation. CONCLUSIONS: QAI shows emerging potential for brain disorder analysis, particularly through hybrid quantum-classical learning, quantum neural architectures, quantum-kernel methods, and quantum-inspired optimization. Nevertheless, current evidence remains preliminary and requires larger datasets, subject-level and external validation, fair classical benchmarking, noise-resilient circuits, real quantum hardware evaluation, explainability, and clinical validation.

Humans↗

PubMed2026

A Sentiment-Based Comparison of AI- and Physician-Generated Empathic Statements in Palliative Care.

CONTEXT: Empathic communication promotes trust in patient-provider relationships. As healthcare integrates artificial intelligence (AI) into patient communication, we have yet to understand how these models' communication compares to that of physicians. OBJECTIVES: Our primary objectives were to examine patient preferences for AI-generated vs. palliative care physician-generated empathic statements addressing fear and anxiety around cancer treatment, and to analyze associations between linguistic features and patient preferences. METHODS: We conducted a secondary analysis of the PALL-AI trial, a randomized controlled survey comparing cancer patients' preferences of AI- to physician-generated empathic statements. Physicians and AI were provided the same prompt with a maximum sentence length. Patient preferences for each statement were measured in blinded surveys. We analyzed sentiment of the statements using the Valence Aware Dictionary and Sentiment Reasoner (VADER) and the National Research Council Canada (NRC) Emotion Lexicon. We evaluated associations between sentiment scores and patient preferences using Spearman's correlation coefficients. RESULTS: A total of 105 patients completed blinded surveys, preferring the AI-generated statement 72.4% of the time. VADER sentiment analysis showed all three AI statements displayed positive sentiment, while all three physician statements displayed negative sentiment. Controlling for statement length, AI statements used twice as many positive words as human statements. However, they contained a similar number of negative words. Of the eight NRC emotions, "trust" and "joy" demonstrated the strongest correlations with patient preference. CONCLUSION: Patients preferred AI-generated statements around cancer care over those from palliative care physicians when standardized for prompt and statement length. Analysis shows AI-generated statements contain more positive language which may be the factor driving patient preference toward AI.

Humans↗

PubMed2026

How AI-supported intelligent systems support infection prevention and control training in healthcare: A systematic review of educational functions and outcomes.

AIMS: Artificial intelligence (AI)-supported intelligent systems have been increasingly incorporated into infection prevention and control (IPC) education and training, primarily to support the monitoring of observable behaviors and the provision of feedback. However, existing evidence has focused largely on short-term compliance outcomes, with limited synthesis of the educational role of AI-supported intelligent systems in supporting sustained IPC competence. This systematic review examined how AI-supported intelligent systems have been designed and used to support IPC education and training, with a focus on system characteristics, educational functions, and reported outcomes. DESIGN: A systematic literature search was conducted across the PubMed/MEDLINE, Embase, Cochrane, and CINAHL databases. DATA SOURCES: A total of 18 studies met the inclusion criteria. Findings were qualitatively synthesized according to system design characteristics, educational functions, and outcome domains. REVIEW METHODS: Methodological quality was appraised using the Mixed Methods Appraisal Tool. RESULTS: Most AI-supported intelligent systems focused on hand hygiene and relied on fully automated monitoring systems to capture behaviors and provide performance feedback. Educational functions were predominantly limited to performance assessment, automated feedback, and reminders. Outcomes were mainly measured using compliance or performance metrics, whereas sustained behavioral change and decision quality were rarely assessed. CONCLUSIONS: AI-supported intelligent systems have been used primarily to reinforce short-term IPC performance and compliance. However, their current applications for supporting sustained competence over time remain limited. The findings of this review suggest that AI-supported intelligent systems may serve as maintenance-oriented educational support by extending learning beyond initial instruction through repeated practice and feedback. Future research should prioritize outcome measures that capture the durability of performance and decision-making processes to better align AI-supported intelligent systems used in IPC education and training with the educational demands of clinical practice.

Humans↗

PubMed2026

Beyond predictive performance: A systematic review and critical methodological appraisal of AI/ML and conventional modelling strategies in breast, colorectal, and pancreatic Cancer.

BACKGROUND: Predictive modelling for cancer risk, treatment-related complications, and survival is central to precision oncology. Conventional logistic regression (LR) and Cox proportional hazards (CoxPH) regression remain widely used but are limited when modelling nonlinear interactions, high-dimensional imaging features, and multimodal clinical-metabolic predictors. Artificial intelligence (AI) and machine learning (ML) methods offer expanded capability through automated feature extraction, ensemble learning, and flexible survival modelling, but the evidence on when AI/ML adds value over conventional models across cancer sites and predictive tasks remains fragmented. OBJECTIVE: To systematically evaluate the methodological performance, validation strategies, and translational limitations of AI/ML models compared with conventional statistical models in published predictive-modelling studies for breast, colorectal, or pancreatic cancer. METHODS: PubMed, Scopus, and Web of Science were searched for studies published between January 2019 and March 2025. Two reviewers independently conducted title-and-abstract screening, full-text eligibility assessment, and PROBAST risk-of-bias assessment. Sixty-five studies (n = 907,567 participants) were narratively synthesised by cancer site, predictive task, model family, comparator, validation strategy, predictor modality, and calibration or explainability reporting. RESULTS: The 65 studies comprised breast cancer (n = 35), colorectal cancer (n = 21), and pancreatic cancer (n = 9). AI/ML superiority over LR and CoxPH was task- and data-dependent. CNN- and U-Net-based models predominated in imaging and body-composition tasks, tree-based ensembles consistently outperformed LR for tabular perioperative complication prediction, and CoxPH remained competitive, and in the largest pancreatic risk study, superior to XGBoost (C-index 0.802 vs 0.723) in well-structured datasets. PROBAST analysis-domain risk was moderate in 54 of 65 studies (83%), driven by limited external validation, sparse calibration reporting (11/65), and few decision-curve analyses (7/65). CONCLUSION: AI/ML adds the most methodological value in imaging-derived feature extraction and nonlinear perioperative prediction, while conventional regression remains preferable in large, structured datasets with linear predictors. Clinical translation requires standardised body-composition definitions, external validation, calibration assessment, decision-curve analysis, and explainability, in line with TRIPOD+AI and CLAIM standards.

Humans↗

PubMed2026

Hysteroscopic platelet-rich plasma and medically assisted reproduction outcomes: a systematic review and SWOT analysis.

BACKGROUND: Platelet-rich plasma (PRP) has been proposed as an adjuvant treatment in reproductive medicine. While most evidence refers to blind intrauterine instillation, subendometrial administration under hysteroscopic guidance allows targeted delivery under direct visualisation. This systematic review aimed to synthesise the available evidence on hysteroscopic PRP administration and its impact on clinical medically assisted reproduction (MAR) outcomes. METHODS: A systematic search was conducted from inception to December 2025 across major databases. Studies were included if they evaluated hysteroscopic PRP administration in women undergoing MAR, comparing reproductive outcomes between treated and control groups. RESULTS: Out of 142 records, 3 studies met the inclusion criteria. Study populations were heterogeneous and included women with refractory thin endometrium and/or a history of implantation failure. Hysteroscopic PRP administration protocols varied in timing, technique, and dosage. In a prospective case-control study, hysteroscopic intraendometrial PRP injection at a depth of 2-3 mm in the four uterine walls, using an ovum aspiration needle, on days 11-13 of the cycle prior to euploid frozen embryo transfer (ET), was associated with higher implantation (IR), clinical pregnancy (CPR), and live birth rates (LBR) compared with standard therapy. Conversely, no significant differences in CPR, miscarriage rate, or LBR were observed in an observational study evaluating a single intraendometrial PRP injection (35-40 mL, 2-3 mm depth), administered via endoscopic needle on days 6-8 of the menstrual cycle preceding frozen ET, alone or after electrical impulse therapy. A randomised controlled trial in women undergoing intrauterine insemination reported a significant improvement in CPR following hysteroscopic subendometrial PRP instillation in the four uterine walls (1.0 mL each). CONCLUSIONS: Current literature on hysteroscopic PRP administration in reproductive medicine is limited, and robust conclusions cannot yet be drawn. Well-designed randomised controlled trials with standardised protocols are needed to clarify its clinical role.

Humans↗

PubMed2025

Enhanced fracture detection on radiographs with AI assistance for clinicians: a systematic review and meta-analysis.

BACKGROUND: Emergency radiographic interpretation for fractures is prone to missed or misdiagnoses. Artificial intelligence (AI) is expected to become a powerful tool to assist clinicians in fracture detection. PURPOSE: A systematic review and meta-analysis was performed to assess whether AI improves clinicians' ability to detect fractures on radiographs. MATERIALS AND METHODS: A literature search was conducted in PubMed, Web of Science, and Cochrane Library for studies published between January 1, 2010, and October 10, 2025. A meta-analysis of diagnostic accuracy studies was performed using a Summary Receiver Operating Characteristic (SROC) curve. The quality of included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. Subgroup analysis and meta-regression were conducted to explore potential sources of heterogeneity. RESULTS: A total of 26 studies were included . The pooled sensitivity of clinicians increased from 77% (95% CI: 72-81) to 87% (95% CI: 83-90) with AI assistance, while the pooled specificity improved from 88% (95% CI: 85-90) to 92% (95% CI: 89-94). The corresponding AUC values were 0.90 (95% CI: 0.87-0.92) before and 0.95 (95% CI: 0.93-0.97) after AI assistance. Eight studies were rated as high risk of bias. Subgroup analysis and meta-regression identified potential sources of heterogeneity, including fracture location, AI model type, high risk of bias, and reference standards. CONCLUSION: AI assistance significantly improves clinicians' diagnostic performance in detecting fractures on radiographs for extremity and trunk fractures.

Humans↗

PubMed2026

Closed-loop insulin delivery for glycaemic control in hospitalised and perioperative adults: A systematic review and meta-analysis of randomised controlled trials.

We evaluated whether closed-loop insulin delivery improves glycaemic control in hospitalised and perioperative adults. PubMed/MEDLINE, Embase, CENTRAL, and ClinicalTrials.gov were searched from inception to 29 June 2026 for randomised controlled trials comparing closed-loop or automated insulin delivery with usual care or conventional insulin therapy. Random-effects meta-analyses were conducted; risk of bias was assessed using RoB 2 and certainty of evidence using GRADE. Seven trials involving 375 analysed participants were included. Closed-loop insulin delivery increased time in target glucose range by 23.91 percentage points (95% CI 19.40 to 28.43; I2 = 0%) and reduced mean glucose by 1.79 mmol/L (95% CI 1.06 to 2.53 lower; I2 = 36.3%); certainty was moderate for both outcomes. Two trials involving 69 participants reported compatible participant-level data for clinically significant hyperglycaemia, and both estimates favoured closed-loop insulin delivery, although the evidence was exploratory and imprecise. No severe hypoglycaemic events occurred in either group, precluding reliable estimation of comparative safety. Closed-loop insulin delivery may improve glycaemic process measures, but larger pragmatic trials are needed to establish clinical benefits, safety, and implementation feasibility.

Humans↗

PubMed2026

Applications of metal-organic frameworks in smart packaging for food freshness indication: a comprehensive review.

Smart packaging is extensively studied for its multifunctional capabilities in antimicrobial activity, preservation, and atmosphere modification. Recently emerged metal-organic frameworks (MOFs) freshness-indicating packaging becomes a key research direction in smart packaging owing to its distinctive functions and physicochemical properties. As multifunctional materials, the unique porous structure and tunable properties of MOFs provide a distinctive approach for developing food packaging applications dedicated to food freshness indication. Existing MOFs-based smart packaging still faces potential safety risks and technical challenges in practical applications, and there remains a lack of integrated discussion that combines synthesis strategies, packaging design, optimization, and safety assessment. This review elaborates on the application of MOFs in freshness-indicating smart packaging, focusing on diverse MOFs synthesis strategies, the formats of smart packaging, types of indicator signals, and qualitative/quantitative analytical methods. It also delves into the methodology concepts of MOFs-based smart packaging and evaluates MOFs safety in food packaging by addressing potential risks. Studies show that MOFs-based smart packaging achieves qualitative and semi-quantitative analysis of food freshness through multiple signal modalities such as visible color change, fluorescence, and photothermal effects. This review emphasizes that safe MOFs design is critically important and should comply with the overall migration limit of <10 mg/dm2 specified in Regulation (EC) No 1935/2004, lanthanide element limit of <0.05 mg/kg, and FDA threshold of 1.5 μg/person/day. Comprehensive safety assessment and intelligent sensing platforms will constitute pivotal directions for advancing MOFs-based smart packaging toward practical application.

Food Packaging↗

PubMed2026

AI Health message intervention: The role of message customization and message source in breast cancer screening among women of color.

OBJECTIVES: To examine the effectiveness of breast cancer screening messages with varying levels of customization (generic, targeted, and tailored) and to compare AI-generated versus human-generated messages. METHODS: A between-subjects experimental design with a control condition was employed. Message content followed a standardized structure and varied by level of customization: generic, targeted (demographic-based), and tailored (perceived susceptibility- and barrier-based). Messages were developed by either the authors or GenAI (ChatGPT-4o). A total of 391 participants recruited via Prolific were randomly assigned to five groups (generic, targeted-human, targeted-AI, tailored-human, and tailored-AI). Self-efficacy, behavioral intentions, attitudes, and message believability were measured using different scales. RESULTS: Customized (tailoring and targeting) health messages performed comparably to generic messages in shaping positive health outcomes. GenAI-generated messages also produced outcomes comparable to those of human-generated messages under standardized conditions. Significant negative indirect effects through message believability for the human-tailored condition was found relative to the generic condition. CONCLUSIONS: GenAI may be a useful tool for developing and customizing scalable health messages. Its effectiveness depends not only on customization but also on maintaining message quality, including readability, clarity, coherence, naturalness, and credibility. PRACTICAL IMPLICATIONS: GenAI may support health practitioners in developing customized and scalable breast cancer messages. However, professional review remains necessary to ensure that the message is culturally appropriate, responsive to patient concerns, and suitable for use alongside patient-provider communication.

Humans↗

PubMed2026

Glycemic and safety outcomes of the insulin-only bionic pancreas in older adults and individuals with impaired awareness of Hypoglycemia: a post hoc analysis of a randomized pivotal trial.

AIMS: Evaluate the efficacy and safety of iLet Bionic Pancreas (BP) in older adults and individuals with impaired awareness of hypoglycemia (IAH). METHODS: This post hoc analysis used individual participant-level data from the Insulin-Only Bionic Pancreas Pivotal Trial (n = 440; NCT04200313). Eligible participants (n = 96) with type 1 diabetes, aged ≥ 60 years and/or had IAH (Clarke score ≥ 4), were randomized to BP with aspart/lispro (BP-Asp/Lis; n = 45), BP with fast-acting aspart configuration (BP-Fiasp; n = 31), or standard care (SC; n = 20) for 13 weeks. RESULTS: Compared with SC, time-in-range (70-180 mg/dL) significantly increased by 7.49 % (95 % CI: 2.61 to 12.38; ∼1.8 h/day) with BP-Asp/Lis and by 8.28 % (95 % CI: 3.15 to 13.41; ∼2.0 h/day) with BP-Fiasp, driven by reduced hyperglycemia. No significant differences were observed in hypoglycemia exposure. Severe hypoglycemia occurred in four participants (four events) on BP-Asp/Lis and one participant (two events) on SC. One diabetic ketoacidosis event occurred on BP-Fiasp due to an infusion set failure. CONCLUSIONS: In high-risk, clinically vulnerable populations, the BP system significantly improved glycemic control while maintaining safety parity with respect to hypoglycemia risk, providing a resilient therapeutic alternative for vulnerable cohorts.

Humans↗

PubMed2026

Interventions with a significant mortality difference in acute respiratory distress syndrome: A systematic review and comparison with Guidelines.

INTRODUCTION: Acute respiratory distress syndrome (ARDS) has a high mortality rate. European Society of Intensive Care Medicine (ESICM) and American Thoracic Society (ATS) Guidelines are the worldwide reference for clinicians in management of ARDS. Mortality represents one of the most important outcomes in intensive care practice and randomized controlled trials (RCTs) the highest level of evidence. We compared Guidelines recommendations with RCT results to highlight differences and find potential new therapeutic opportunities. METHODS: We performed a systematic review of all RCTs reporting a statistically significant mortality difference in ARDS and a subsequent comparison with ESICM and ATS Guidelines recommendations. RESULTS: We identified 33 RCTs and 23 interventions with mortality difference in ARDS patients. Seven interventions relate to invasive ventilation strategies, two to noninvasive ventilation strategies, one to extracorporeal membrane oxygenation (ECMO), 12 to drugs and one to nutritional support. In 25/33 (76%) RCTs the intervention was associated with mortality reduction and in 8/33 with mortality increase (24%). Multicenter studies were 24/33 (73%) while blinding was adopted in 19/33 (58%) studies. Guidelines recommendations supported by RCTs with mortality impact include: the use of low tidal volume ventilation, prone positioning, venovenous ECMO, steroids and the avoidance of high frequency oscillatory ventilation. Eight of the interventions identified were not mentioned by Guidelines but demonstrated reduced mortality, and five further interventions demonstrated increased mortality. CONCLUSIONS: This systematic review highlights potential gaps between RCTs results and Guidelines that could be used to plan future research or highlight topics to be discussed in future Guidelines.

Humans↗

PubMed2026

Anabolic androgen therapy in critically ill adults: A systematic review and meta-analysis.

Critical illness is characterized by a catabolic, proinflammatory state. Anabolic agents, such as testosterone, have therefore been proposed as therapeutic targets. Our objectives were to assess the effects of testosterone in critically ill populations on patient-important outcomes and identify design limitations to inform future studies. We searched for randomized control trials (RCTs) through Medline, Embase, and EBM Reviews databases from inception through February 24, 2026, including English language articles enrolling adults (≥18 years) admitted to ICU where anabolic androgen therapies (AAT) were compared with placebo or standard of care. Studies had to report at least one of: mortality, ICU and hospital lengths of stay, or duration of mechanical ventilation. We extracted data independently using a standardized data extraction tool, and feedback was received from all co-authors to ensure agreement. For each outcome, we performed meta-analyses using a random-effects model with inverse variance weighting in RevMan. We used the GRADE approach to assess certainty in pooled estimates of effect. Of 1325 screened articles, we found 4 that fit our inclusion criteria. Together, we judged risk of bias as 'some concerns' in 3 trials and 'high' in the final trial, and ultimately found that the effects of anabolic-androgen therapy on patient-important outcomes uncertain. With the uncertainty of current evidence for the effects of anabolic-androgen therapy in critically ill adults, there is insufficient support for its routine use. Future randomized evidence is needed to determine whether anabolic-androgen therapy improves clinically-important outcomes and better define its safety profile in critically ill adults.

Humans↗

PubMed2026

Risk factors of venous thromboembolism in ICU patients: a systematic review and meta-analysis.

OBJECTIVE: This study aimed to identify risk factors associated with the development of VTE in patients admitted to the intensive care unit (ICU). METHODS: A systematic literature search was conducted via PubMed, Embase, Web of Science, and Cochrane databases up to 25 April 2025, to identify studies examining the association between risk factors and the occurrence of venous thromboembolism (VTE) in ICU patients. Data were pooled using odds ratios (ORs) and 95% confidence intervals (CIs). RESULTS: A total of 2465 relevant studies were identified through the systematic search, of which 30 were included in the meta-analysis. The pooled data showed that the following were significant risk factors for venous thromboembolism (VTE) in ICU patients: central venous catheterization (OR = 2.67, 95% CI: 1.67-4.28; I2 = 28%), invasive mechanical ventilation (OR = 2.08, 95% CI: 1.46-2.96; I2 = 0%), advanced age (OR = 2.06, 95% CI: 1.28-3.31; I2 = 86%), length of ICU stay (OR = 4.24, 95% CI: 1.43-12.57; I2 = 98%), malignancy (OR = 2.30, 95% CI: 1.03-5.12; I2 = 67%), elevated D-dimer levels (OR = 2.46, 95% CI: 1.37-4.40; I2 = 34%), and a history of VTE (OR = 2.84, 95% CI: 1.45-5.55; I2 = 51%). According to the GRADE assessment, the quality of evidence was rated as moderate for invasive mechanical ventilation, low for central venous catheterization and D-dimer levels, and very low for the remaining factors. CONCLUSION: Invasive mechanical ventilation, central venous catheterization, and elevated D-dimer levels are associated with VTE risk, supported by relatively high-quality evidence. These findings may help identify ICU patients at higher risk of VTE, inform the development of risk assessment models for patient stratification, and ultimately contribute to improved prognosis through optimal screening and management strategies.

Humans↗

PubMed2026

Comparison of VCV and PCV-VG modes on diaphragmatic function in diabetic patients undergoing laparoscopic colorectal surgery: a prospective randomized controlled study.

BACKGROUND: Diabetic patients are prone to induce diaphragmatic weakness, which can lead to postoperative pulmonary complications (PPCs). The optimal mechanical ventilation mode may potentially improve postoperative diaphragmatic function. This study evaluates the effects of two ventilation modes under driving pressure-guided ventilation strategy on diaphragmatic function, as assessed by diaphragm thickening fraction (DTF) and diaphragm excursion (DE), in diabetic patients following laparoscopic colorectal surgery. METHODS: Eighty patients diagnosed with Type II diabetes scheduled for elective laparoscopic colorectal surgery, were randomly allocated to either the pressure-controlled volume-guaranteed ventilation (PCV-VG) group (Group P) or the volume-controlled ventilation (VCV) group (Group V) during surgery. The primary outcome was diaphragmatic function assessed during both tidal breathing and maximal inspiratory effort after surgery. Secondary outcomes included intraoperative mechanical power, PPCs, and other complications. RESULTS: A total of eighty patients were included in the final analysis. The averaged area under the curve (AUC) for mechanical power during ventilation was significantly lower in Group P than in Group V (p = 0.002). PCV-VG significantly improved both DE and DTF within the first two days post-surgery (AUCDEtidal: p = 0.088, AUCDTFtidal: p = 0.004, AUCDEmax: p = 0.029, AUCDTFmax: p = 0.017). Postoperative diaphragmatic weakness was less frequent in Group P than in Group V (p = 0.019). However, there was no difference in the incidence of PPCs between the two groups (p = 0.155). CONCLUSION: PCV-VG mode can reduce intraoperative mechanical power, better preserve postoperative diaphragmatic function. However, these improvements did not translate into clinical benefits, as evidenced by the lack of reduction in the incidence of PPCs.

Humans↗

PubMed2026

Effects of permissive hypercapnia on intraoperative cerebral oxygenation and early postoperative cognitive function in older patients with fragile brain function during the non-acute phase undergoing laparoscopic colorectal surgery: A randomized controlled trial.

BACKGROUND AND PURPOSE: Older adults with non-acute fragile brain function (NFBF) may be particularly susceptible to perioperative disturbances in cerebral oxygenation and postoperative neurocognitive decline. Permissive hypercapnia (PHC) may enhance cerebral oxygenation, but its effects in this population remain unclear. We examined whether PHC-based ventilation improves intraoperative regional cerebral oxygen saturation (rSO2) and early postoperative cognitive outcomes in older patients with NFBF undergoing elective laparoscopic colorectal surgery. METHODS: In this single-center, single-blind randomized trial, 76 patients were assigned in a 1:1 ratio to PHC-based or conventional ventilation. The primary outcome was the absolute change in rSO2 from baseline (T0) to the end of surgery (T4). Analyses followed the intention-to-treat principle, with prespecified per-protocol sensitivity analysis. Secondary outcomes included intraoperative rSO2 trajectories, cerebral oxygen extraction-related indices, early postoperative cognitive screening, serum neuron-specific enolase and interleukin-6, and safety outcomes. RESULTS: PHC significantly increased rSO2 relative to conventional ventilation (left: adjusted mean difference [aMD] 10.64, 95% CI 8.96-12.33; right: aMD 10.16, 95% CI 8.22-12.11; both P < 0.001), with consistent sensitivity results. Repeated-measures analyses showed persistently higher intraoperative rSO2 in the PHC group. Cerebral oxygen extraction-related indices were generally lower with PHC. However, early postoperative cognitive outcomes and serum biomarkers did not differ between groups. Emergence time was modestly longer with PHC, whereas adverse events were comparable. CONCLUSIONS: PHC-based ventilation favorably modified intraoperative cerebral oxygenation and oxygen-extraction profiles but did not translate into detectable early postoperative cognitive or biomarker benefits in older adults with NFBF.

Humans↗

PubMed2026

Effectiveness of an AI-based home exercise app for rehabilitation of rotator cuff-related shoulder pain: A randomized controlled trial.

BACKGROUND: Rotator cuff-related shoulder pain contributes to disability and healthcare use. Although therapeutic exercise is first-line treatment, limited supervision and adherence may reduce its effectiveness; digital rehabilitation with real-time feedback may address these limitations. OBJECTIVES: To evaluate the effectiveness of adding a digital rehabilitation program to standard physiotherapy on pain, function, fear-avoidance beliefs, and healthcare utilization. DESIGN: Single-center, assessor-blinded, randomized controlled trial with two parallel groups. METHOD: Forty-six adults (mean age 59 years) with rotator cuff-related shoulder pain were randomized to 12 weeks of conventional physiotherapy or physiotherapy plus an AI-based digital rehabilitation program using computer vision for real-time feedback and performance monitoring. Outcomes were assessed at baseline and at 2, 4, and 12 weeks. Pain intensity (NPRS) was primary outcome; secondary outcomes included upper limb function (QuickDASH), fear-avoidance beliefs (FABQ), and post-intervention healthcare utilization. Analyses followed an intention-to-treat approach. RESULTS: Pain reduction exceeded the MCID (1.3) at 4 and 12 weeks. Between-group differences favoured the intervention at Weeks 2 and 4 (MD -0.7; 95% CI -1.13 to -0.14 and MD -1.01; 95% CI -1.8 to -0.2, respectively). Upper limb function improved more at Week 4 (MD -7.3; 95% CI -12.3 to -2.2). FABQ scores decreased more at Week 12 (MD -7.6; 95% CI -14 to -0.5). Fewer participants in the experimental group required post-intervention healthcare (3 vs 10; p = 0.02). CONCLUSION: Adding AI-based home exercise app to conventional treatment improve pain and may improve function and reduce healthcare utilization in rotator cuff-related shoulder pain.

Humans↗