Search Results (2,415)

Indoor air pollution has become a significant concern, contributing to the decline in air quality through the presence of gaseous pollutants and particulate matter, especially under poor ventilation. Hospitals, functioning as non-industrial microenvironments, particularly in Thailand, face challenges due to insufficient and incomplete databases for effective air quality management. Within these environments, patients with heightened sensitivity, along with hospital staff who are predominantly exposed indoors, face increased risk of exposure to indoor air pollutants. This study aimed to review current evidence on VOCs in hospital settings in Thailand, identifying their sources, concentrations, and health impacts. It also aimed to provide recommendations for improved air quality monitoring and management. The review included studies published between 2008 and 2023 in English or Thai. Studies were selected based on relevance to VOCs in hospital environments, while excluding those lacking sufficient data or methodological rigor. Literature searches were conducted using Google Scholar, ScienceDirect, Scopus, and PubMed. Results from international studies were also considered to address gaps. Data extraction focused on VOC sources, concentrations, measurement methods, and associated health impacts. Results were synthesized into six thematic categories: characterization, health effects, control measures, etiological studies, monitoring systems, and comparative studies. The review identified 87 relevant studies. VOC exposure was associated with several adverse health impacts resulting from short- and long-term exposures, leading to an increased risk of cancer. Identified sources of VOC emissions within hospitals encompass anesthetic gases, sterilization processes, pharmaceuticals, laboratory chemicals, patient care, and household products, as well as building materials and furnishings. Commonly encountered VOCs include alcohols (e.g., ethanol, 2-methyl-2-propanol, isopropanol), ether, isoflurane, nitrous oxide, sevoflurane, chlorine, formaldehyde, aromatic hydrocarbons, limonene, and glutaraldehyde, among those commonly detected in hospital environments. Yet, limited knowledge exists regarding their source contributions, emissions, and concentrations associated with health impacts in Thai hospitals. Full article

Background/Objectives: Acute myocardial infarction (MI) is a major cause of death worldwide, and it imposes a heavy burden on health care systems. Although diagnostic methods have improved, detecting the disease early and accurately is still difficult. Recently, AI has demonstrated increasing capability in improving ECG-based MI detection. From this perspective, this scoping review aimed to systematically map and evaluate AI applications for detecting MI through ECG data. Methods: A systematic search was performed in Ovid MEDLINE, Ovid Embase, Web of Science Core Collection, and Cochrane Central. The search covered publications from 2015 to 9 October 2024; non-English articles were included if a reliable translation was available. Studies that used AI to diagnose MI via ECG were eligible, and studies that used other diagnostic modalities were excluded. The review was performed per the PRISMA extension for scoping reviews (PRISMA-ScR) to ensure transparent and methodological reporting. Of a total of 7189 articles, 220 were selected for inclusion. Data extraction included parameters such as first author, year, country, AI model type, algorithm, ECG data type, accuracy, and AUC to ensure all relevant information was captured. Results: Publications began in 2015 with a peak in 2022. Most studies used 12-lead ECGs; the Physikalisch-Technische Bundesanstalt database and other public and single-center datasets were the most common sources. Convolutional neural networks and support vector machines predominated. While many reports described high apparent performance, these estimates frequently came from relatively small, single-source datasets and validation strategies prone to optimism. Cross-validation was reported in 57% of studies, whereas 36% did not specify their split method, and several noted that accuracy declined under inter-patient or external validation, indicating limited generalizability. Accordingly, headline figures (sometimes ≥99% for accuracy, sensitivity, or specificity) should be interpreted in light of dataset size, case mix, and validation design, with risks of spectrum/selection bias, overfitting, and potential data leakage when patient-level independence is not enforced. Conclusions: AI-based approaches for MI detection using ECGs have grown quickly. Diagnostic performance is limited by dataset and validation issues. Variability in reporting, datasets, and validation strategies have been noted, and standardization is needed. Future work should address clinical integration, explainability, and algorithmic fairness for safe and equitable deployment. Full article

Background/Objectives: Epigenetics refers to heritable modifications in gene expression that do not involve changes to the DNA sequence. Among these, DNA methylation, histone modifications, and non-coding RNAs play a key role in regulating gene activity and are influenced by environmental factors, including exposure to psychoactive substances. In recent years, it has been hypothesized that such alterations may serve as molecular markers with forensic relevance. This systematic review aims to evaluate whether current evidence supports the use of drug-induced epigenetic changes as potential toxicological fingerprints in human subjects. Methods: A systematic literature search was conducted following PRISMA guidelines, including articles published on PubMed between 1 January, 2010, and 31 December, 2025. Only studies conducted on human samples and published in English were considered; animal studies and articles lacking epigenetic data were excluded. Results: Forty-two studies met the inclusion criteria. The most commonly investigated substances (alcohol, cocaine, methamphetamine, cannabis, and opioids) were found to induce specific and, in some cases, persistent epigenetic changes. These include alterations in CpG methylation in promoter regions, variations in miRNA expression, and modulation of epigenetic enzymes. Such changes were observed in brain tissue, blood cells, and semen, with evidence of persistence even after drug cessation. Conclusions: Current evidence confirms that psychoactive substance use is associated with specific epigenetic modifications. However, forensic application remains limited due to confounding factors such as age, co-exposures, and post-mortem interval. Further standardized research is necessary to validate their use as forensic biomarkers. Full article

The European spruce bark beetle, Ips typographus (Linnaeus, 1758), poses a significant threat to Picea abies (Linnaeus) Karsten, 1881 forests, with outbreaks often exacerbated by abiotic disturbances like the 2018 Vaia windstorm in the Italian Alps. Pheromone-baited traps are widely used for control, yet their overall efficacy and potential side effects, particularly the incidental capture of non-target insects, remain debated. This study aimed to comprehensively assess the presence and composition of non-target insects in I. typographus pheromone traps, used for both mass-trapping and monitoring, in the affected Alpine regions. We took into account single monitoring traps (dry collection) and three-trap cross configurations for mass-trapping (with preservative liquid), collecting and morphologically identifying insect by-catch. Our results revealed a non-target proportion (excluding bark beetles) significantly higher in mass-trapping (4.15%) compared to monitoring (1.00%), with approximately half being natural enemies of bark beetles. Crucially, we report that bark beetle parasitoids were repeatedly caught, with Tomicobia seitneri (Ruschka, 1924) (the third most abundant non-target species) particularly well represented, and Ropalophorus clavicornis (Wesmaël, 1835) also detected, which is noteworthy given its ecological role despite its lower numbers. Our findings underscore the significant, previously underreported, capture of beneficial parasitoids and highlight the need for careful consideration of non-target catches in I. typographus pest management strategies. Full article

Objective: Non-intubated video-assisted thoracoscopic surgery (NIVATS) has emerged as a less invasive alternative to conventional intubated VATS (IVATS) for patients undergoing lobectomy for non-small cell lung cancer (NSCLC). However, concerns regarding its safety, efficacy, and oncologic adequacy remain. This meta-analysis aimed to compare perioperative and short-term outcomes between NIVATS and IVATS. Methods: A systematic review and meta-analysis were conducted in accordance with PRISMA guidelines. PubMed, Scopus, and Cochrane CENTRAL were searched through 30 June 2025. Studies comparing NIVATS and IVATS for anatomical lung resections (lobectomy and/or segmentectomy) in NSCLC were included; wedge resections were excluded. Primary endpoints included postoperative complications, operative time, intraoperative blood loss, lymph node yield, and 30-day mortality. Secondary endpoints were chest tube duration, hospital length of stay, anesthetic time, and conversion to thoracotomy rates. Risk of bias was assessed primarily with ROBINS-I; the Newcastle–Ottawa Scale was applied for sensitivity. Results: A total of seven studies (six retrospective and one randomized controlled trial) encompassing 851 patients (374 NIVATS, 477 IVATS) were included. NIVATS was associated with a significantly lower rate of postoperative complications (OR 0.50; 95% CI: 0.30–0.86; p = 0.01; I² = 0%), shorter operative time (minutes) (WMD −21.85; 95% CI: −38.49, −5.21; p = 0.01), anesthetic time (minutes) (WMD −4.62; 95% CI: −6.60, −2.65; p < 0.01), and reduced intraoperative blood loss (mL) (WMD −24.36; 95% CI: −30.67, −18.05; p < 0.01). There were no significant differences in lymph node yield or conversion to thoracotomy rates. No 30-day mortality was reported in either group. The quality of included studies was moderate, and publication bias was not evident. Conclusions: NIVATS appears to be a safe and effective alternative to IVATS in selected patients undergoing lobectomy for NSCLC. It offers improved perioperative outcomes without compromising surgical or oncologic standards. Prospective trials are needed to confirm these findings and assess long-term survival. Full article

Background: Topical tranexamic acid (TXA), often combined with epinephrine, is used to reduce perioperative bleeding. This systematic review evaluates the safety and effectiveness of this combination across surgical procedures. Methods: A comprehensive search of eight databases was conducted from inception to 26 June 2025. Studies were eligible if they compared topically or locally applied TXA with epinephrine to epinephrine alone in surgical patients. Animal studies, case reports, non-English publications, and studies without comparators were excluded. Screening, data extraction, and risk of bias assessments followed PRISMA guidelines. Results: Ten studies met inclusion criteria (four randomized and six non-randomized), covering burn surgery, rhytidectomy, liposuction, septoplasty, endoscopic sinus surgery, dacryocystorhinostomy, and joint arthroplasty. TXA was applied topically or via tumescent infiltration. Most studies reported reduced intraoperative blood loss, improved surgical field visibility, lower drain output, shorter hemostasis time, and reduced transfusion rates. No increase in thromboembolic or major complications was observed. Conclusion: The combination of TXA and epinephrine appears safe and maybe effective for perioperative bleeding control. However, heterogeneity in dosing and outcomes limits generalizability. Further research is needed to standardize protocols and confirm long-term safety. Full article

The mechanisation of sesame (Sesamum indicum L.) planting remains a significant challenge due to the crop’s small, fragile seeds and non-uniform shape, which hinder the effectiveness of standard seeding systems. Crop emergence and production are adversely affected by poor singulation and uneven seed distribution, which are frequently caused by conventional and general-purpose planting equipment. For sesame, consistency in seed distribution and emergence is very important, necessitating careful consideration of agronomic conditions as well as seed properties. This study was conducted as a systematic review following the PRISMA 2020 guidelines to critically evaluate the existing literature on advanced planting methods that prioritise precision, efficiency, and seed protection. A comprehensive search was conducted across Scopus, Web of Science, and Google Scholar for peer-reviewed studies published from 2000 to 2025. Studies focused on the agronomic parameters of sesame, planting technologies, and/or simulation integration, such as Discrete Element Modelling (DEM), were included in this review, and studies unrelated to sesame planting or not available in full text were excluded. The findings from these studies were analysed to examine the interaction between seed metering mechanisms and seed morphology, specifically seed thickness and shape variability. Agronomic parameters such as optimal seed spacing, sowing depth, and population density are analysed to guide the development of effective planting systems. The review also evaluates limitations in existing mechanised approaches while highlighting innovations in precision planting technology. These include optimised seed plate designs, vacuum-assisted metering systems, and simulation tools such as DEM for performance prediction and system refinement. A total of 22 studies were included and analysed using systematic narrative synthesis, grouped into agronomical, technological, and simulation-based themes. The studies were screened for methodological clarity, and reference list screening was performed to reduce reporting bias. In conclusion, the findings of this research support the development of crop-specific planting strategies tailored to meet the unique requirements of sesame production. Full article

Background: Customer churn significantly impacts business revenues. Machine Learning (ML) and Deep Learning (DL) methods are increasingly adopted to predict churn, yet a systematic synthesis of recent advancements is lacking. Objectives: This systematic review evaluates ML and DL approaches for churn prediction, identifying trends, challenges, and research gaps from 2020 to 2024. Data Sources: Six databases (Springer, IEEE, Elsevier, MDPI, ACM, Wiley) were searched via Lens.org for studies published between January 2020 and December 2024. Study Eligibility Criteria: Peer-reviewed original studies applying ML/DL techniques for churn prediction were included. Reviews, preprints, and non-peer-reviewed works were excluded. Methods: Screening followed PRISMA 2020 guidelines. A two-phase strategy identified 240 studies for bibliometric analysis and 61 for detailed qualitative synthesis. Results: Ensemble methods (e.g., XGBoost, LightGBM) remain dominant in ML, while DL approaches (e.g., LSTM, CNN) are increasingly applied to complex data. Challenges include class imbalance, interpretability, concept drift, and limited use of profit-oriented metrics. Explainable AI and adaptive learning show potential but limited real-world adoption. Limitations: No formal risk of bias or certainty assessments were conducted. Study heterogeneity prevented meta-analysis. Conclusions: ML and DL methods have matured as key tools for churn prediction, yet gaps remain in interpretability, real-world deployment, and business-aligned evaluation. Systematic Review Registration: Registered retrospectively in OSF. Full article

Background/Objectives: To develop a new Virtual Reality (VR) system software for measuring ocular deviation in strabismus patients. Methods: This prospective study included subjects with basic-type exotropia (XT) and non-refractive accommodative esotropia (ET). Ocular deviation was measured using the alternate prism cover test (APCT) and two VR-based methods: target offset (TO) and a newly developed camera rotation (CR) method. Results: A total of 28 subjects were recruited (5 cases were excluded for preliminary testing and 5 for not meeting inclusion criteria). Among the 18 included patients, 10 (66.7%) had XT and 5 (33.3%) had ET. The median age was 21.5 years (IQR 17 to 25). The mean age was 22.3 years (range: 9–46), with 5 (27.8%) having manifest strabismus and 12 (61.1%) measured while wearing glasses. VR-based methods (TO and CR) showed comparable results to APCT for deviation angle measurements (p = 0.604). Subgroup analysis showed no significant differences in ET patients (all p > 0.05). In XT patients, both TO and CR underestimated deviation angles compared to APCT (p = 0.008 and p = 0.001, respectively), but no significant difference was observed between the two methods (p = 0.811). Linear regression showed CR had a stronger correlation with APCT than TO (R² = 0.934 vs. 0.874). Conclusions: This newly developed VR system software, incorporating the CR method, provides a reliable approach for measuring ocular deviation. By shifting the entire visual scene rather than just the target, it lays a strong foundation for immersive diagnostic and therapeutic VR applications. Full article

Background: Severe traumatic brain injury (sTBI) in adults remains a leading cause of mortality and disability worldwide. Early identification of reliable predictors of outcome is crucial for risk stratification and ICU management. Disturbances of hemostasis and metabolic factors such as body mass index (BMI) have been proposed as potential prognostic markers, but evidence remains limited. Methods: We conducted a retrospective, multicenter study including 307 adult patients with sTBI (Glasgow Coma Scale ≤ 8) admitted to three tertiary intensive care units in Ukraine between September 2023 and July 2024. All patients underwent surgical evacuation of hematomas and decompressive craniotomy. Laboratory parameters (APTT, INR, fibrinogen, platelets, D-dimer) were collected within 12 h of admission. BMI was calculated from measured height and weight. Predictive modeling was performed using L1-regularized logistic regression and Random Forest algorithms. Class imbalance was addressed with SMOTE. Model performance was assessed by AUC, accuracy, calibration, and feature importance. Results: The 28-day all-cause mortality was 32.9%. Compared with survivors, non-survivors had significantly lower GCS scores and higher INR, D-dimer, and APTT values. Very high VIF values indicated severe multicollinearity between predictors. Classical logistic regression was not estimable due to perfect separation; therefore, regularized logistic regression and Random Forest were applied. Random Forest demonstrated higher performance (AUC 0.95, accuracy ≈ 90%) than logistic regression (AUC 0.77, accuracy 70.1%), although results must be interpreted cautiously given the small sample size and potential overfitting. Feature importance analysis identified increased BMI, prolonged APTT, and elevated D-dimer as leading predictors of mortality. Sensitivity analysis excluding BMI still yielded strong performance (AUC 0.91), confirming the prognostic value of coagulation markers and GCS. Conclusions: Mortality in adult sTBI patients was strongly associated with impaired hemostasis, obesity, and low neurological status at admission. Machine learning-based modeling demonstrated promising predictive accuracy but is exploratory in nature. Findings should be interpreted with caution due to retrospective design, severe multicollinearity, potential overfitting, and absence of external validation. Larger, prospective, multicenter studies are needed to confirm these results and improve early risk stratification in severe TBI. Full article

Background/Objectives: Interpretation of mixture profiles generated from crime scene samples is an important element in forensic genetics. Here, a workflow for mixture deconvolution of sequenced microhaplotypes (MHs) and STRs using the probabilistic genotyping software MPSproto v0.9.7 was developed, and the performance of the two types of loci was compared. Methods: Sequencing data from a custom panel of 74 MHs (the MH-74 plex) and a commercial kit with 26 autosomal STRs (the ForenSeq™ DNA Signature Prep Kit) were used. Single-source profiles were computationally combined to create 360 two-person and 336 three-person mixtures using the Python script MixtureSimulator v1.0. Additionally, 72 real mixtures typed with the MH-74 plex and 18 real mixtures typed with the ForenSeq Kit from a previous study were deconvoluted using MPSproto. Results: The deconvoluted MH profiles were more complete and had fewer wrong genotype calls than the deconvoluted STR profiles. The contributor proportion estimates were more accurate for MH profiles than for STR profiles. Wrong genotype calls were mostly caused by locus and heterozygous imbalances, noise reads, or an inaccurate contributor proportion estimation. The latter was especially problematic in STR sequencing data, when two contributors contributed equally to the mixture. A total of 34,800 deconvolutions of the simulated mixtures were performed with two defined hypotheses: H_p, “The sample consists of DNA from one/two unknown contributor(s) and the suspect” and H_d, “The sample consists of DNA from two/three unknown individuals”. All true contributors were identified (LR > 10¹⁵ for MHs and LR > 10⁹ for STRs) and all non-contributors excluded (LR < 10⁻⁶ for MHs and LR < 0.2 for STRs). Conclusions: In simulated and real mixtures, the MHs performed better than STRs. Full article

Background/Objectives: The aim of the study was to investigate the risk factors related to early implant failures in patients treated in a private implant clinic. Methods: The study was retrospectively conducted on 392 patients (mean age = 51.2 ± 15.4 years, 48.9% female and 51.1% male) who received 930 dental implants within the period from 2000 to 2020. Included patients had received at least one implant. Patients were excluded in case of incomplete dental records that did not contain the necessary information, including personal information (patient’s name, age, gender, history and current condition of systemic diseases) and treatment protocol record. No patients were excluded on the basis of systemic disease if it did not contraindicate dental implant surgery. Patient-related variables (age, gender, smoking, history of periodontitis, accompanying disease), surgical-related variables (surgical technique, bone augmentation, lateral sinus lift, internal sinus lift, immediate implant placement after tooth extraction, immediate mucosal grafting, insertion torque,) and implant-related variables (implant design, implant level, implant surface, site of implant placement, implants diameter, length and implants brand name) were recorded. Results: GEE analysis results showed that the implant-based failure rate before or at the abutment connection stage was 5.8%. Seven factors were identified associated with early implant failures: male gender, smoking, history of radiotherapy and chemotherapy, maxilla implant placement, non-submerged healing method, implant design and implant brand. Conclusions: In light of the results obtained, both patient-related variables (gender, smoking, history of radiotherapy and chemotherapy) and variables related to the type of implant, its position and the surgical technique used (maxilla implant placement, non-submerged healing method, implant design and implant brands) were found to be statistically correlated with early failures in this study. Full article

Background/Objectives: Diabetic retinopathy is a leading cause of blindness in diabetic patients, with disease susceptibility influenced by both genetic and environmental factors. This study aimed to identify novel genetic variants associated with DR and evaluate interactions between polygenic risk scores (PRS) and lifestyle factors in a Korean diabetic cohort. Methods: After excluding subjects with non-diabetic retinopathy eye diseases (n = 2519), we analyzed data from 50,361 non-diabetic controls, 4873 diabetic participants without retinopathy (DM-NR), and 165 with diabetic retinopathy (DM-DR). We conducted genome-wide association studies comparing DM-NR and DM-DR groups, performed generalized multifactor dimensionality reduction (GMDR) analysis for epistatic interactions, developed unweighted PRS models, and examined PRS–lifestyle interactions using two-way analysis of covariance. Results: DM-DR prevalence showed strong associations with metabolic syndrome and its components. Five novel genetic variants were identified: ABCA4_rs17110929, MMP2-AS1_rs2576531, FOXP1_rs557869288, MRPS33_rs1533933, and DRD2_rs4936270. A significant three-way epistatic interaction among the first three variants was discovered through GMDR analysis. High-PRS individuals (scores 5–6) showed a 49-fold higher odds ratio of DM-DR compared to low-PRS individuals (scores 0–2; p < 0.0001). MAGMA analysis revealed enrichment in pathways related to protein degradation, vascular function, and neuronal signaling, with predominant upregulation in brain tissues. Significant PRS × lifestyle interactions were identified for fruit intake, coffee consumption, alcohol intake, eating duration, and physical activity, with lifestyle factors modifying genetic risk effects (all p < 0.003). Conclusions: These findings identify novel genetic variants and epistatic interactions in DM-DR pathogenesis, supporting the use of PRS-based risk stratification for intensive monitoring and personalized lifestyle interventions. The discovery of brain tissue-enriched pathways suggests DM-DR shares mechanisms with neurodegenerative diseases, expanding therapeutic targets beyond traditional vascular approaches. Full article

Refractory neuropathic pain of the head and neck remains a major clinical challenge, particularly when mediated through the cervicotrigeminal complex (CTC), a unique anatomical hub integrating trigeminal and upper cervical nociceptive inputs. This narrative review synthesizes neuroanatomical, pathophysiological, and clinical evidence to provide a unifying framework for diagnosis and management. A structured search of PubMed, Scopus, and Web of Science identified English-language clinical and mechanistic studies addressing CTC-mediated pain, with case reports excluded unless mechanistically informative. We propose multidimensional refractoriness criteria that integrate pharmacological non-response, failed interventional strategies, and objective functional impairment. Current treatments span pharmacotherapy, peripheral interventions (nerve blocks, radiofrequency ablation), and neuromodulation at multiple network levels (occipital nerve stimulation, spinal cord stimulation, motor cortex stimulation, deep brain stimulation). Non-invasive approaches such as rTMS, tDCS, and vagus nerve stimulation are emerging but remain investigational. Advances in imaging and neurophysiological biomarkers now permit greater precision in detecting CTC dysfunction and tailoring therapy. By combining anatomical precision, mechanistic insight, and multidisciplinary strategies, this review proposes a clinically actionable definition of refractoriness and supports a stepwise, mechanism-based approach to therapy. CTC emerges as a targetable hub for diagnostic and therapeutic strategies in refractory head and neck pain. Full article

Background: Stigmatizing attitudes toward individuals with mental health conditions are common among healthcare professionals in primary healthcare (PHC) settings, posing a major barrier to early diagnosis, appropriate treatment, and recovery. Methods: This systematic review and meta-analysis evaluated the effectiveness of interventions aimed at reducing mental health-related stigma among PHC professionals (general practitioners, nurses, community health workers, and allied providers). Eligibility was restricted to interventional studies targeting PHC staff; non-clinical populations and students without clinical practice were excluded. Comparators included usual training, waitlist control, or pre–post evaluation. A systematic search of PubMed, Scopus, and Web of Science was conducted in accordance with PRISMA guidelines, and the protocol was registered in PROSPERO (CRD420251074412). Results: Twenty-five studies met the inclusion criteria, of which three contributed to the quantitative synthesis. Interventions included educational, contact-based, and multicomponent approaches. Risk of bias was assessed using tools appropriate to study design. Interventions generally improved knowledge and attitudes and, to a lesser extent, behavioral intentions. Meta-analysis of pre–post changes using the Opening Minds Scale for Health Care Providers (OMS-HC) demonstrated a significant reduction in stigma (MD = −0.27, 95% CI −0.40 to −0.14; p < 0.001; I² = 91%). A difference-in-differences analysis of studies with intervention and control groups confirmed this effect with moderate heterogeneity (MD = −0.18, 95% CI −0.25 to −0.11; p < 0.0001; I² = 50%). Conclusions: Contact-based and multicomponent interventions were associated with stronger and more sustained effects. The main limitations of the evidence were short follow-up periods, reliance on self-reported outcomes, methodological heterogeneity, and the possibility of publication bias. Our findings suggest that reducing stigma among PHC professionals can enhance patient engagement, timely diagnosis, and quality of care in routine clinical practice. Full article