Comprehensive Assessment Research Articles

Learning to quantify uncertainty in off-target activity for CRISPR guide RNAs.

CRISPR-based genome editing technologies have revolutionised the field of molecular biology, offering unprecedented opportunities for precise genetic manipulation. However, off-target effects remain a significant challenge, potentially leading to unintended consequences and limiting the applicability of CRISPR-based genome editing technologies in clinical settings. Current literature predominantly focuses on point predictions for off-target activity, which may not fully capture the range of possible outcomes and associated risks. Here, we present crispAI, a neural network architecture-based approach for predicting uncertainty estimates for off-target cleavage activity, providing a more comprehensive risk assessment and facilitating improved decision-making in single guide RNA (sgRNA) design. Our approach makes use of the count noise model Zero Inflated Negative Binomial (ZINB) to model the uncertainty in the off-target cleavage activity data. In addition, we present the first-of-its-kind genome-wide sgRNA efficiency score, crispAI-aggregate, enabling prioritization among sgRNAs with similar point aggregate predictions by providing richer information compared to existing aggregate scores. We show that uncertainty estimates of our approach are calibrated and its predictive performance is superior to the state-of-the-art in silico off-target cleavage activity prediction methods. The tool and the trained models are available at https://github.com/furkanozdenn/crispr-offtarget-uncertainty.

Wetting Preference of Silica Surfaces in the Context of Underground Hydrogen Storage: A Molecular Dynamics Perspective.

The growing interest in large-scale underground hydrogen (H2) storage (UHS) emphasizes the need for a comprehensive understanding of the fundamental characteristics of subsurface environments. The wetting preference of subsurface rock is a crucial parameter influencing the H2 flow behavior during storage and withdrawal processes. In this study, we utilized molecular dynamics simulation to evaluate the wetting preference of the silica surface in subsurface hydrogen systems, with the aim of addressing disparities observed in experimental results. We conducted an initial comprehensive assessment of potential models, comparing the wettability of five common silica surfaces with different surface morphologies and hydroxyl densities in CO2-H2/water/silica systems against experimental data. After introducing the INTERFACE force field as the most accurate potential model for the silica surface, we evaluated the wetting behavior of the α-quartz (101) surface with a hydroxyl density of 5.9 number/nm2 under the impact of actual geological storage conditions (333-413 K and 10-30 MPa), the coexistence of cushion gases (i.e., CO2, CH4, and N2) at various mole fractions, and pH levels ranging from 2 to 11 characterized through considering the negative charges of 0 to -0.12 C/m2 via deprotonation of silanol on the silica surface. Our results indicate that neither pressure nor temperature has a significant impact on the wetness of the silica in the case of pure H2 (single component UHS operations). However, when CO2 coexists with H2, especially at higher mole fractions, an increase in pressure and a decrease in temperature lead to higher contact angles. Moreover, when the mole fraction of cushion gas ranges from 0 to 1, the contact angle increases 20, 9.5, and 4.5° for CO2, CH4, and N2, respectively, on the neutral silica substrate. Interestingly, at higher pH conditions where the silica surface carries a negative charge, the contact angle considerably reduces where surface charges of -0.03 and -0.06 C/m2 result in an average reduction of 20 and 80% in the contact angle, respectively. More importantly, at a pH of ∼11 (-0.12 C/m2), a 0° contact angle is observed for the silica surface under all temperatures, pressures, types of cushion gases, and varying mole fractions.

Cumulative collision risk and population-level consequences of industrial wind-power plant development for two vulture species: A quantitative warning

Prioritizing renewable energy generation over the conservation of natural habitats and species on a large spatial scale, leads to the paradox of impacting biodiversity to mitigate climate change. In this study, we aim at quantifying the long-term demographic impact of the excess mortality caused by collisions with wind turbines on the populations of two vulture species of conservation concern. Using long-term monitoring data and Integrated Population Models (IPMs), we quantified demographic parameters and projected population trends under various wind power development scenarios. Our findings indicate that even under our most optimistic scenarios, annual collision mortality could reach up to 30 % of the current Cinereous vulture population and 7 % of the Griffon vulture population. Without further wind power expansion, both vulture populations are predicted to remain stable or increase over the next 20 years. However, the addition of 85 wind turbines is likely to drive the Cinereous vulture to local extinction within 18 years and significantly slow the growth of the Griffon vulture population. Scenarios involving larger numbers of turbines could result in the extinction of both species within two to five years for Cinereous vultures and up to 20 years for Griffon vultures, depending on space use intensity. Our results underscore the vulnerability of long-lived species to excess mortality and highlight the need for comprehensive Environmental Impact Assessments (EIAs) that incorporate population dynamics analyses. Effective conservation strategies must include rigorous pre- and post-construction monitoring, the availability of monitoring data, and cumulative impact assessments that consider the entire foraging range of these species. Additionally, strategic planning to avoid critical vulture habitats and implementing mitigation measures in buffer zones are essential. This study emphasizes the necessity of integrating biodiversity considerations into renewable energy planning to balance the goals of energy production and wildlife conservation.

A new approach for risk assessment of pulmonary toxicants: a preliminary study using didecyldimethylammonium chloride.

Current human risk assessments often rely on animal toxicity data to establish point of departure (POD) values, followed by the application of uncertainty factors. Consequently, there is growing interest in alternative toxicity testing methods that reduce reliance on animal models. In this study, we propose a novel approach for inhalation toxicity risk assessment that integrates in silico and in vitro methods. Human primary alveolar epithelial cells were exposed to aerosolized didecyldimethylammonium chloride (DDAC) to assess cytotoxicity. This was followed by transcriptome analysis and biological pathway investigation, utilizing adverse outcome pathway (AOP), to calculate the POD. Additionally, human DDAC exposure was simulated using a multiple-path particle dosimetry (MPPD) model to predict exposure levels in the human alveolar region via inhalation. The results from in silico and in vitro studies were then compared, and a comprehensive risk assessment was performed. The POD for AOP 452 key events-oxidative stress, inflammation, epithelial-mesenchymal transition (EMT), apoptosis, and autophagy-was found to range between 19.0 and 23.89ng/cm2, according to benchmark dose calculation tools. The estimated human exposure to DDAC in the alveolar region under actual exposure conditions was 0.164ng/cm2/day, resulting in a margin of exposure (MOE) ranging from 121 to 145, suggesting caution regarding DDAC inhalation exposure. This study presents a novel risk assessment method that compares estimated human inhalation exposure values to in vitro results, applying the human equivalent concentration concept. Our findings demonstrate the potential for conducting human risk assessments using both in silico and in vitro methods as alternatives to traditional in vivo studies.

Comprehensive Assessment of Context-Adaptive Street Lighting: Technical Aspects, Economic Insights, and Measurements from Large-Scale, Long-Term Implementations

Comprehensive Assessment of Context-Adaptive Street Lighting: Technical Aspects, Economic Insights, and Measurements from Large-Scale, Long-Term Implementations

Comprehensive Assessment of Climate Change Impacts on River Water Availability for Irrigation, Wheat Crop Area Coverage, and Irrigation Canal Hydraulic Capacity of Large-Scale Irrigation Scheme in Nepal

Comprehensive Assessment of Climate Change Impacts on River Water Availability for Irrigation, Wheat Crop Area Coverage, and Irrigation Canal Hydraulic Capacity of Large-Scale Irrigation Scheme in Nepal

Hepatocellular Carcinoma Immune Microenvironment Analysis: A Comprehensive Assessment with Computational and Classical Pathology.

The spatial variability and clinical relevance of the tumour immune microenvironment (TIME) are still poorly understood for hepatocellular carcinoma (HCC). Here we aim to develop a deep learning (DL)-based image analysis model for the spatial analysis of immune cell biomarkers, and microscopically evaluate the distribution of immune infiltration. Ninety-two HCC surgical liver resections and 51 matched needle biopsies were histologically classified according to their immunophenotypes: inflamed, immune-excluded, and immune-desert. To characterise the TIME on immunohistochemistry (IHC)-stained slides, we designed a multi-stage DL algorithm, IHC-TIME, to automatically detect immune cells and their localisation in TIME in tumour-stromal, centre-border segments. Two models were trained to detect and localise the immune cells on IHC-stained slides. The framework models, i.e. immune cell detection models and tumour-stroma segmentation, reached 98% and 91% accuracy, respectively. Patients with inflamed tumours showed better recurrence-free survival than those with immune-excluded or immune desert tumours. Needle biopsies were found to be 75% accurate in representing the immunophenotypes of the main tumour. Finally, we developed an algorithm that defines immunophenotypes automatically based on the IHC-TIME analysis, achieving an accuracy of 80%. Our DL-based tool can accurately analyse and quantify immune cells on IHC-stained slides of HCC. The microscopical classification of the TIME can stratify HCCs according to the patient prognosis. Needle biopsies can provide valuable insights for TIME-related prognostic prediction, albeit with specific constraints. The computational pathology tool provides a new way to study the HCC TIME.

Non-Word Repetition in Arabic-speaking children with and without Autism Spectrum Disorder (ASD): A closer look into accuracy and error patterns

ABSTRACT Non-Word Repetition (NWR) tasks effectively identify language impairments and assess phonological skills across diverse populations and languages, including Arabic. Prior research revealed heterogeneity of performance in children with Autism Spectrum Disorder (ASD) on NWR tasks. The current study is the first to evaluate phonological skills of Palestinian-Arabic-speaking children with and without ASD, employing a Palestinian-Arabic NWR task. A total of 142 Palestinian-Arabic-speaking children, aged 5–11 participated in the study, including 75 children with Typical Language Development (TLD) and 67 children with ASD. The NWR task included 18 non-words of varying length (1–4 syllables) and complexity (with and without consonant clusters). Quantitative analysis examined the effects of length and phonological complexity on the NWR accuracy scores in children with TLD and ASD. Error pattern analysis accounting for phoneme and syllable substitutions/deletions/additions and lexicalisations aimed to shed light on the phonological representations of children with and without ASD. Within the ASD group, two subgroups emerged: 72% exhibited age-appropriate NWR performance, while 28% showed performance at-risk for phonological impairment. Non-word length, rather than complexity, significantly influenced the children’s performance. Consonant substitution was the most frequent error pattern across all groups. On the theoretical side, these findings extend cross-linguistic evidence of phonological skill heterogeneity in children with ASD to Arabic-speaking children. Additionally, they highlight an overall delayed but qualitatively similar pattern of phonological development in children with ASD. On the clinical side, results underscore the importance of comprehensive language assessment in children with ASD.

Increased risk of depression and associated symptoms in poststroke aphasia.

Poststroke aphasia hinders patients' emotional processing and social adaptation. This study estimated the risks of depression and related symptoms in patients developing or not developing aphasia after various types of stroke. Using data from the US Collaborative Network within the TriNetX Diamond Network, we conducted a retrospective cohort study of adults experiencing their first stroke between 2013 and 2022. Diagnoses were confirmed using corresponding International Classification of Diseases, Tenth Revision, Clinical Modification codes. Patients were stratified by poststroke aphasia status and stroke type, with propensity score matching performed to control for confounders. The primary outcome was depression within one year post-stroke; secondary outcomes included anxiety, fatigue, agitation, emotional impact, and insomnia. Each matched group comprised 12,333 patients. The risk of depression was significantly higher in patients with poststroke aphasia (hazard ratio: 1.728; 95% CI 1.464-2.038; p < 0.001), especially those with post-hemorrhagic-stroke aphasia (hazard ratio: 2.321; 95% CI 1.814-2.970; p < 0.001). Patients with poststroke aphasia also had higher risks of fatigue, agitation, and emotional impact. Anxiety and insomnia risks were higher in those with post-hemorrhagic-stroke aphasia. Poststroke aphasia, particularly post-hemorrhagic-stroke aphasia, may increase the risks of depression and associated symptoms, indicating the need for comprehensive psychiatric assessments.

Comprehensive resilience assessment of bridge networks using ensemble learning method

The assessment of seismic resilience in bridge networks holds significant importance for urban disaster prevention and mitigation efforts. Unlike individual bridges, there has been limited efficiency in assessing bridge networks. A seismic resilience assessment methodology for bridge networks using ensemble learning methods is proposed in this paper. Initially, a comprehensive resilience index is proposed, integrating both structural and functional aspects of bridge networks. Using 3 ensemble learning methods, 9 parameters related to network structure and traffic characteristics are chosen as input variables for predicting the seismic resilience index. Finite element models of 18 bridges are constructed and combined to generate 3500 sets of virtual bridge networks for model training. The predictive accuracy of models trained using the 3 ensemble methods exceeds 89 %, and the expected values of peak ground acceleration (PGA) and functional loss rate are the most influential features. The methodology offers insights into the application of ensemble learning for bridge network seismic resilience assessment.

Developing circularity metrics for the maritime industry: A stakeholder focused study

Embracing circular practices is crucial for reducing the maritime industry's environmental impact. However, the industry lacks tailored circular economy metrics to effectively measure its circularity practices and performance. To address this gap, a comprehensive review was conducted, analysing over 400 circularity metrics used in various other industries. From this extensive review, these metrics were refined and adapted to develop 57 circularity metrics tailored to the maritime sector. These novel metrics aim to guide maritime stakeholders in assessing and improving their circularity performance. Their relevance was validated through stakeholder engagement and consultations, and practicality was demonstrated through a detailed case study in three private shipyards. This case study showcased the metrics' applicability and highlighted their potential benefits in real-world scenarios by providing suggestions for improving the circularity performance of the facilities. This study marks the first comprehensive maritime circularity assessment, providing a structured pathway for the future adoption and implementation of circularity principles within the maritime industry. By establishing these metrics, the study sets a foundational framework for stakeholders to enhance sustainability and promote circular economy practices in maritime operations. Quantifying circular economy performance will enable stakeholders to track progress, identify best practices, and drive transition.

Comprehensive assessment of fragrance profiles in peony flowers through HS-GC-IMS and metabolic pathway analysis

This study addresses the identification and comparison of volatile organic compounds (VOCs) in different peony varieties to elucidate their distinct aroma profiles and the common VOCs that constitute the unique fragrance of peony flowers, laying a foundation for breeding high-quality peony varieties and developing related products. Headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS) combined with principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) was employed to analyze and differentiate the VOCs of eight peony varieties: TAOXI, ZIHELIAN, BAITIANE, YAOHUANG, DAFUGUI, DAOJIN, HONGXIUQIU, and MANAOPAN. The study identified 87 VOCs, revealing significant differences in VOC compositions among the samples. Notably, BAITIANE displayed distinct differences in both color and VOC composition compared to the other seven varieties. It exhibited uniquely high levels of butanoic acid-M, butanoic acid-D, hexanal-D, ethanol-D, and ethyl acetate. Twelve key differential VOCs were identified as having significant impacts: ethanol-D, hexanal-D, (e)-2-hexenal-D, (e)-2-hexenal-M, hexanal-M, butanoic acid-M, acetic acid-D, acetic acid-M, butanoic acid-D, ethyl acetate, thiophene, and dimethyl sulfide. KEGG pathway analysis highlighted the roles of xenobiotics biodegradation, terpenoid metabolism, and amino acid metabolism in VOC biosynthesis. The findings are significant for the breeding of fragrant peony varieties and the development of floral-based products. The findings provide theoretical support for breeding new horticultural flower varieties and developing commercial peony products, enhancing the understanding of floral scent chemistry, and offering valuable information for the perfumery, floral food processing, and related industries.

Comprehensive Assessment of Lipid Markers in Cardiovascular Events Prediction.

Many studies have reported a relationship between various lipids, such as cholesterol, fatty acids, and lipoproteins, and cardiovascular events. Low-density lipoprotein cholesterol (LDL-C) is often cited as a representative marker. However, there is still room for discussion regarding which markers, among other lipids, should take clinical precedence.This observational study focused on patients without residual stenosis on post-coronary angiography. It was based on blood tests, including lipid profiles at that time, and assessed the association with the subsequent occurrence of major adverse cardiovascular events (MACE, a composite of all-cause mortality, hospitalization due to heart failure, myocardial infarction, stroke, and all revascularizations).Of the 375 patients analyzed, 134 experienced MACE (median follow-up duration: 1031 days). When comparing the MACE and non-MACE groups, significant differences were observed in lipid markers such as non-high-density lipoprotein cholesterol (non-HDL-C) and remnant-like particle cholesterol (RLP-C) (non-HDL-C; P = 0.003, RLP-C; P < 0.001). Furthermore, the area under the curve for RLP-C was 0.656 (95% CI: 0.598-0.714). Improvement in MACE risk discrimination was observed when LDL-C was replaced with non-HDL-C or RLP-C, in addition to atherosclerosis risk factors (non-HDL-C; net reclassification improvement (NRI) = 0.366, 95% CI: 0.159-0.572, RLP-C; NRI = 0.224, 95% CI: 0.016-0.433).It is highly likely that non-HDL-C and RLP-C can serve as significant lipid markers for predicting the occurrence of MACE.

Idiopathic Distal Sensory Polyneuropathy and Fibromyalgia Syndrome: A Comparative Phenotyping Study.

Painful idiopathic distal sensory polyneuropathy (IDSP) and fibromyalgia syndrome (FMS) are cryptogenic chronic pain syndromes. The contribution of small fibre pathology (SFP) in FMS remains controversial. This study aims to quantify small nerve pathology in participants with IDSP and FMS and identify relationships of SFP with sensory phenotypes. In this study, 73 individuals (FMS: 25, IDSP: 23, healthy volunteers: 25) underwent comprehensive assessment, including neurological exams, questionnaires, sensory tests, and corneal confocal microscopy. IDSP participants displayed lower wind-up ratio (WUR) relative to FMS (p<0.001), loss of function to thermal and mechanical stimuli and elevated neuropathy disability scores compared to FMS and healthy volunteers (all p<0.001). FMS participants demonstrated gain of function to heat and blunt pressure pain responses relative to IDSP, and healthy volunteers (heat: p=0.002 and p=0.003; pressure: both p<0.001) and WUR (both p<0.001). FMS participants exhibited reduced corneal nerve fibre density (p=0.02), while IDSP participants had lower global corneal nerve measures (density, branch density, and length) relative to healthy volunteers (all p<0.001). Utilising corneal nerve fibre length, SFP was demonstrated in 66.6% of participants (FMS: 13/25; IDSP: 22/23). Participants with SFP,in both FMS and IDSP, reported symptoms indicative of small nerve fibre disease. Although distinctions in pain distributions are evident between individuals with FMS and IDSP, over 50% of participants between the two conditions displayed both a loss and gain of thermal and mechanical function suggestive of shared mechanisms. However, sensory phenotypes were associated with the presence of SFP in IDSP but not in FMS.

Effect of smoking on the recurrence and progression of non-muscle-invasive bladder cancer.

It is well established that smoking is the most significant risk factor for bladder cancer, yet the impact of smoking on the recurrence and progression of non-muscle-invasive bladder cancer (NMIBC) remains a contentious issue. To review all relevant literature published to date, providing a comprehensive assessment of the effects of smoking on the recurrence and progression of NMIBC, thereby offering a basis for smoking cessation management in NMIBC patients. A search was conducted for all relevant literature published up to April 2024 in PubMed, Web of Science, and Embase databases. The existing literature results and deficiencies were analyzed, and the gaps in understanding between different studies were highlighted, with recommendations for future research. A total of 24 studies were included in this work. Among them, 14 studies suggested that smoking promotes the recurrence and progression of NMIBC, while another 10 studies concluded that smoking has no effect on the recurrence and progression of NMIBC patients. Our research indicates that smoking increases the risk of recurrence and progression in NMIBC patients, and quitting smoking can improve health-related quality of life. High-quality, large-sample prospective cohort studies (or randomized controlled studies) are still needed in the future.

Curriculum, competency development, and assessment methods of MSc and PhD pharmacy programs: a scoping review

Background/objectivesWe aim to systematically review and evaluate the current landscape of postgraduate pharmacy education to a) identify current evidence, best practices, challenges, recommendations, and solutions; and b) develop a framework to optimize postgraduate pharmacy programs.MethodsA scoping review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). Electronic databases, including PubMed, Scopus, EMBASE, ProQuest, Web of Science, and Google Scholar were utilized. The search covered studies published from January 2011 to September 2023. Following the principles of Arksey and O'Malley's framework, data charting and extraction were performed using a pre-designed data collection tool, followed by the synthesis and grouping of studies based on common themes.ResultsOf the 5542 articles found, the review included 36 eligible ones focusing on pharmacy postgraduate education (PhD and MSc), grouped into three themes: 1) courses and curriculum; 2) training and skills development; 3) assessment and mentorship methods. Utilized methodologies included descriptive analyses, questionnaires, surveys, trials, and focus groups/interviews. The studies underscored the need for competency-based curricula with regular evaluations, career planning, and diverse course offerings. Identified key skills and competencies in the studies included soft skills, communication, research, desperate skills (e.g., leadership and management), and critical thinking. The studies also emphasized the value of comprehensive evaluation and peer review methods. Challenges included balancing academic and real-world requirements, training, limited resources, time constraints, and faculty workload.ConclusionEvidence-based suggestions to improve postgraduate pharmacy education include the implementation of practice-oriented courses, value of tailored/or comprehensive assessments, focus on real-world skills, effectiveness of advanced teaching methods, and mentorship role. The proposed framework can guide program enhancement and highlight the need to improve programs holistically, entailing the three themes.

Enhancing therapy with nano-based delivery systems: exploring the bioactive properties and effects of apigenin.

Apigenin, a potent natural flavonoid, has emerged as a key therapeutic agent due to its multifaceted medicinal properties in combating various diseases. However, apigenin's clinical utility is greatly limited by its poor water solubility, low bioavailability and stability issues. To address these challenges, this review paper explores the innovative field of nanotechnology-based delivery systems, which have shown significant promise in improving the delivery and effectiveness of apigenin. This paper also explores the synergistic potential of co-delivering apigenin with conventional therapeutic agents. Despite the advantageous properties of these nanoformulations, critical challenges such as scalable production, regulatory approvals and comprehensive long-term safety assessments remain key hurdles in their clinical adoption which must be addressed for commercialization of apigenin-based formulations.

Risk factors for early and late retinal detachment after boston type I keratoprosthesis surgery.

To determine the differential risk factors for retinal detachment (RD) after Boston Type 1 Keratoprosthesis (B1KPro) during the first year after surgery (early RD; ERD) and afterwards (late RD; LRD). Retrospective cohort study of 94 eyes (90 patients) undergoing B1KPro implantation at Centro de Oftalmología Barraquer from June 2006 to July 2022 with a minimum follow-up of one year. The incidence of RD in the whole sample after B1KPro implantation was 29% (27/94), occurring in 21% of eyes (ERD; 20/94) the first year, and in 7% afterwards (LRD; 7/94). Hypotony was revealed as a significant risk factor for ERD (16/19 = 84%; OR = 14.5, P < 0.0001), together with aphakia (16/20 = 80%; OR = 5.9, P = 0.004), intraocular lens (IOL) removal (4/8 = 50%; OR = 6.2, P = 0.03), previous choroidal detachment (CD; 7/20 = 35%; OR = 35.5, P = 0.001) and previous pars plana vitrectomy (PPV; 15/20 = 75%; OR = 6.6, P = 0.006). Multivariate analysis included hypotony and PPV at any time as variables significantly associated with ERD occurrence. The occurrence of LRD was significantly related to peripheral retinal changes, detected few days before (4/6 = 66%; OR = 65, P < 0.0001). Hypotony emerged as one of the main factors influencing ERD occurrence after B1KPro implantation. Additionally, previous CD, PPV, aphakia, and IOL removal should be considered to estimate the risk of postoperative retinal detachment. In contrast, peripheral retinal changes after B1KPro seem to be significantly related to LRD occurrence. This study reinforces the need for frequent intraocular pressure evaluation, and comprehensive periodic retinal assessment over time. What is known Retinal detachment (RD) is a severe complication after Boston Type 1 Keratoprosthesis (B1KPro) implantation, resulting in a permanent reduction of visual acuity in a clinically relevant percentage of eyes. The time of RD occurrence after B1KPro implantation differs in the literature but is reported to be more common within the first year. Previous aphakia, choroidal detachment, or RD repair, intraocular lens removal, vitritis, or history of Nd-YAG laser have been described as risk factors for RD after B1KPro surgery in samples with varying follow-ups. Multivariate analysis in the first year after B1KPro surgery showed hypotony and pars plana vitrectomy at any time as significant risk factors for early RD. Late RD cases, occurring after one year following the B1KPro surgery, seem to be significantly related to peripheral retinal changes suffered a few days before loss of visual acuity and RD diagnosis. It is plausible to think that risk factors for RD after B1KPro differ in short and long postoperative times.

The complete anatomy of the azygos vein: a meta-analysis with clinical implications.

The azygos vein (AV) plays a crucial role in the mediastinal region, exhibiting considerable variability in its anatomy and relationship with surrounding structures. This study aims to assess the morphometry and anatomy of the AV through a comprehensive meta-analysis of studies reporting extractable data on this vessel. Major online medical databases such as PubMed, Scopus, ScienceDirect, Web of Science, SciELO, BIOSIS, Current Content Connect, Korean Journal Database and Wiley online library were searched to gather all relevant studies regarding the anatomical characteristics of the AV. The results of the present meta-analysis comprised 40 studies, categorized into eight groups for data analysis. The mean AV diameter at its origin was set at 3.86 mm (SE = 0.84). The most prevalent was type IIB, with a prevalence of 40.23% (95% CI: 29.06-51.92%). The pooled prevalence of the right subcostal and right ascending lumbar veins forming the AV was 73.82% (95% CI: 55.77-88.67%). The AV exhibits a high degree of variability regarding its origin, trajectory, and connections with the hemiazygos system. The most prevalent type of AV, according to the Anson and McVay classification, was Type II (transitional type). Moreover, the vein was found to be formed by the right subcostal and the right ascending lumbar veins in the majority of the cases. This is the most comprehensive and current assessment of AV morphometry and anatomy to date. The findings are a valuable resource for physicians, especially surgeons performing various procedures in the mediastinum.

Prediction of individual weight loss using supervised learning: Findings from the CALERIE™ 2 study

BackgroundPredicting individual weight loss responses to lifestyle interventions is challenging but might help practitioners and clinicians select the most promising approach for each individual. ObjectiveThe primary aim of this study was to develop machine learning models to predict individual weight loss responses using only variables known before starting the intervention. In addition, we used machine learning to identify pre-intervention variables influencing the individual weight loss response. MethodsWe used 12-month data from the Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy (CALERIE™) phase 2 study, which aimed to analyze the long-term effects of caloric restriction on human longevity. Based on data from 130 subjects in the intervention group, we developed classification models to predict binary (“Success” and “No/low success”) or multi-class (“High success,” “Medium success,” and “Low/no success”) weight loss outcomes. Additionally, regression models were developed to predict individual weight change (percent). Models were evaluated based on accuracy, sensitivity, specificity (classification models), and root mean squared error (regression models). ResultsBest classification models used 20-40 predictors and achieved 89-97% accuracy, 91-100% sensitivity, and 56-86% specificity for binary classification. For multi-class classification, accuracy (69%) and sensitivity (50%) tended to be lower. The best regression performance was obtained with 36 variables with a root mean squared error of 2.84%. Among the 21 variables predicting individual weight change most consistently, we identified two novel predictors, namely orgasm satisfaction and sexual behavior/experience. Other common predictors have previously been associated with weight loss (16) or are already used in traditional prediction models (3). ConclusionsThe prediction models could be implemented by practitioners and clinicians to support the decision of whether lifestyle interventions are sufficient or more aggressive interventions are needed for a given individual, thereby supporting better, faster, data-driven, and unbiased decisions. Trial registrationThe CALERIE™ phase 2 study was registered at clinicaltrials.gov as NCT00427193.