Severity Classification Research Articles

Incorporating the concept of overtransfusion into hemovigilance monitoring: An expert-based definition and criteria from the International HIT-OVER Forum.

Liberal or overtransfusion (OT) may be regarded as "inappropriate," but it is not reported as a transfusion-related adverse event. A definition of OT is lacking. OT may include overdosing of components, giving the incorrect component, or unnecessary administration without evidence of need for transfusion. OT can be associated with hypercoagulability, thrombosis, alloimmunization, increased mortality, longer hospital stay, increased infection rates, and adverse cardiocirculatory events. In 2023, an expert panel formed a hemovigilance international taskforce embedded in the German Interdisciplinary Taskforce for Clinical Hemotherapy (IAKH). The group was charged with proposing simple criteria to be used by hemovigilance systems to document instances of OT. This international initiative combined a narrative review of the literature for the rate and outcomes of OT with transfusion error reports to propose a definition for OT, including a definition for transfusion-induced hypercoagulopathy (TIH), three new codes for OT/TIH and subcodes A to G, three severity categories (serious adverse event, adverse event, near miss), and four incident codes (definite, probable, possible, not determinable). These codes can be used by hemovigilance systems to appropriately document instances of OT. Global adoption of these codes within hemovigilance systems would assist with the recognition and reporting of instances of OT, promote effective policies for adequate clinical administration techniques, and support technical guidelines for avoidance of OT. Thereby, incorporation of OT into hemovigilance strategies could support adequate use of blood products, increase patient safety, and facilitate blood supply and availability.

Pengaruh Kombinasi Aromaterapi Lemon Dan Akupresur Terhadap Penurunan Mual dan Muntah Pada Ibu Hamil Trimester I di Praktek Mandiri Bidan Suryati Kabupaten Jayapura

Nausea and vomiting are common symptoms experienced by pregnant women, especially in the first trimester, and can interfere with the quality of life and well-being of the mother. Although this condition is often considered normal, excessive nausea and vomiting can lead to dehydration, electrolyte imbalance, and weight loss, which is a risk to the health of the mother and fetus. The combination of lemon aromatherapy and acupressure has long been known as a non-invasive method that can help reduce nausea and vomiting. The purpose of the study was to evaluate the effect of the combination of lemon aromatherapy and acupressure on the reduction of nausea and vomiting in pregnant women in the first trimester at the Suryati Midwifery Independent Practice, Jayapura Regency. The results showed that before the intervention was given, the majority of respondents in the intervention and control groups experienced nausea and vomiting in the moderate and severe categories. After the intervention for 7 days with a frequency of 3 times a day, the intervention group experienced a significant decrease in the rate of nausea and vomiting, switching to the moderate and mild categories. In contrast, the control group that did not receive the intervention showed no significant change, with the majority remaining in the category of moderate and severe nausea and vomiting. Statistical analysis showed that there was a significant difference in the intervention group with a p-value of 0.001 (p < 0.05), while there was no significant difference in the control group (p-value 0.564). These findings confirm the effectiveness of the combination of lemon aromatherapy and acupressure as a safe and effective alternative in reducing nausea and vomiting in pregnant women in the first trimester.

Morphological and Mechanical Property Differences in Trapeziometacarpal Ligaments of Healthy and Osteoarthritic Female Joints.

To identify changes in morphological and mechanical properties in the volar ligament complex (VLC), dorsoradial ligaments (DRL), and posterior oblique ligaments (POL) in healthy and osteoarthritic female trapeziometacarpal (TMC) joints. Twenty-four fresh-frozen female cadaveric TMCs were separated into (1) younger healthy/early-stage osteoarthritic, (2) elder healthy/early-stage osteoarthritic, and (3) advanced-stage osteoarthritic groups based on age and Eaton-Littler grading. Stress relaxation and load-to-failure testing were performed to characterize mechanical tensile properties. Light imaging and scanning electron microscopy (SEM)/energy dispersive spectroscopy (EDS) were performed to further assess enthesis structural integrity. The VLC in advanced-stage osteoarthritic TMCs had attenuated mechanical properties in stress relaxation experiments compared to the elder healthy/early-stage osteoarthritic specimens: Young's modulus at 20% strain (P = 0.044), instantaneous (P = 0.023), relaxed (P = 0.017) moduli. VLCs in advanced-stage osteoarthritic TMCs also had significantly lower properties in the load-to-failure experiments compared to the younger healthy/early-stage osteoarthritic specimens: stiffness (P = 0.048), ultimate load (P = 0.017), toughness (P = 0.003). Light and SEM/EDS imaging revealed partial detachment and loss of enthesis mineral gradient at VLC metacarpal insertion in advanced-stage osteoarthritic specimens. There were no mechanical or structural changes in the DRL and POL between experiment groups. VLC morphological and mechanical properties deteriorate across progressively severe osteoarthritis classifications while the DRL and POL remain unchanged. The attenuated mechanical properties of VLCs in advanced-stage osteoarthritic TMCs can be explained by ligament degradation as evidenced by partial detachment and loss of mineral gradient at the metacarpal insertion.

Historical Research on Aerosol Number Concentrations, Classifications of Air Pollution Severity and Particle Retention: Lessons for Present-Day Researchers

Research into the adverse health effects of air pollution exposure has repeatedly considered smaller particles, to the point where particle number concentration might be a more relevant metric than mass concentration. Here, we highlight some historical research which developed metrics for air pollution severity based on particle number concentration. Because this work was published in a national journal and prior to the internet and open access, this historical research is not easy to find, and it was more through the history of the aerosol research community in Ireland that this work is now being presented. Multiple online searches for published research papers on “particle number concentrations” and “air pollution severity” were undertaken. Even when specific searches were undertaken using the author names and publication year, these featured papers were not found on any internet search. O’Dea and O’Connor proposed that air pollution severity could be classified based on particle number concentration of condensation nuclei, with ‘little’ air pollution <50 × 103 particles per cm3, ‘mean’ 50–70 × 103 particles per cm3, ‘strong’ 70–100 × 103 particles per cm3, and ‘very strong’ >100 × 103 particles per cm3. Applying their assumptions on density and mean particle size, equated to mass concentrations for a mean of 6 µgm−3, strong at 8.5 µgm−3, and very strong >10 µgm−3. These are consistent with the current WHO guideline values for PM2.5. Additionally, we highlight the 1955 work by Burke and Nolan on the retention of inhaled particles, where ~40% of the inhaled number concentration is retained in the respiratory system. This is also consistent with the more recently published work on particle retention. In summary, the proposed categories of pollution severity, based on number concentrations, could form a basis for the development of future guidelines. This paper highlights that sometimes research has already been published, but it is difficult to find. We challenge researchers to find publications from their own countries which pre-date the WWW to inform current and future research. Additionally, there is scope for a repository for such information on historical publications. We have presented historical research on aerosol number concentrations, classifications of air pollution severity, and particle retention, which present lessons for current researchers.

Study of Prevalence of Premenstrual Dysphoric Disorder in Nursing Students

Background: Premenstrual dysphoric disorder (PMDD) is a common yet underdiagnosed mental health problem among women of reproductive age group with a significant potential to perturb social, academic, occupational, and interpersonal milieu of suffering women. The aim of this study is to see the Prevalence of PMDD in Nursing Students. Methods: A cross-sectional questionnaire-based study was conducted among the nursing students of the Acharya Shri Chander College of Medical Sciences Nursing School, Jammu and they were screened for the presence of PMDD using the Premenstrual Symptoms Screening Tool. Results: Prevalence of moderate to severe Premenstrual Syndrome (PMS) was 28.1% and PMDD was 6.1%. The commonest premenstrual symptom among the cases was fatigue or lack of energy. In the moderate to severe PMS category, it was anger, and in the PMDD category, it was anxiety. Impairment of college efficiency, or productivity was in 81.3% and 100.0% of students having moderate to severe PMS and PMDD, respectively. Conclusion: PMS and PMDD were prevalent among a substantial proportion of nursing students with significant negative influence on academic performance, emotional well-being and behavior. Strategies should be adopted in college health programs for the timely recognition and management of PMS and PMDD in nursing students.

DINOV2-FCS: a model for fruit leaf disease classification and severity prediction

IntroductionThe assessment of the severity of fruit disease is crucial for the optimization of fruit production. By quantifying the percentage of leaf disease, an effective approach to determining the severity of the disease is available. However, the current prediction of disease degree by machine learning methods still faces challenges, including suboptimal accuracy and limited generalizability.MethodsIn light of the growing application of large model technology across a range of fields, this study draws upon the DINOV2 visual large vision model backbone network to construct the DINOV2-Fruit Leaf Classification and Segmentation Model (DINOV2-FCS), a model designed for the classification and severity prediction of diverse fruit leaf diseases. DINOV2-FCS employs the DINOv2-B (distilled) backbone feature extraction network to enhance the extraction of features from fruit disease leaf images. In fruit leaf disease classification, for the problem that leaf spots of different diseases have great similarity, we have proposed Class-Patch Feature Fusion Module (C-PFFM), which integrates the local detailed feature information of the spots and the global feature information of the class markers. For the problem that the model ignores the fine spots in the segmentation process, we propose Explicit Feature Fusion Architecture (EFFA) and Alterable Kernel Atrous Spatial Pyramid Pooling (AKASPP), which improve the segmentation effect of the model.ResultsTo verify the accuracy and generalizability of the model, two sets of experiments were conducted. First, the labeled leaf disease dataset of five fruits was randomly divided. The trained model exhibited an accuracy of 99.67% in disease classification, an mIoU of 90.29%, and an accuracy of 95.68% in disease severity classification. In the generalizability experiment, four disease data sets were used for training and one for testing. The mIoU of the trained model reached 83.95%, and the accuracy of disease severity grading was 95.24%.DiscussionThe results demonstrate that the model exhibits superior performance compared to other state-of-the-art models and that the model has strong generalization capabilities. This study provides a new method for leaf disease classification and leaf disease severity prediction for a variety of fruits. Code is available at https://github.com/BaiChunhui2001/DINOV2-FCS.

Evolution of Sleep Apnea and Arousals in Patients with Ischemic Stroke: A Longitudinal Follow-Up Study.

Sleep-disordered breathing (SDB) is characterized by pauses or shallow breathing during sleep. It is linked to poorer outcomes and increased stroke recurrence in stroke patients. We aimed to evaluate the proportion, severity, type, and evolution of SDB and arousals in ischemic stroke patients. Ischemic stroke patients were recruited to evaluate SDB. Patients underwent overnight polysomnography (PSG) twice - within 1 month of stroke onset and again after 3 months. Clinical outcomes were evaluated at 6, 18, and 24 months. Of 141 acute ischemic stroke patients screened, 111 patients recruited in the study and 105 patients had technically adequate polysomnography. The mean age of patients was 50.5 years (standard deviation [SD]: 12.30), 78.4% of the patients were males, and the mean body mass index was 26 (SD: 5.4). Results showed that 81.9% had an apnea-hypopnea index (AHI) ≥5, with 12.5% having an AHI >30. The median arousal index (ArI) was 15.4 (interquartile range [IQR]: 0.5-50.5), which increased as the apnea increased. During follow-up, 56.8% patients had an AHI ≥5, with 12.5% having severe apnea. The median ArI decreased from 15.4 (IQR: 0.5-50.5) initially to 11.54 (IQR: 0.5-21.1) at follow-up. Numerical change in AHI severity categories were noted during the follow-up (mild: 8 vs 15; moderate: 17 vs 11; severe 19vs 7). For AHI >30, ArI decreased from 24.63 (IQR: 0.7-50.05) to 10.54 (IQR: 7.1-15.07) at follow-up. Sleep efficiency and rapid eye movement (REM) sleep duration increased on follow-up. The study showed a high proportion of SDB in acute ischemic stroke patients. In the follow-up, SDB improved, as reflected in AHI and ArI changes.

OCTN2 expression and function in the Sertoli cells of testes from patients with non-obstructive azoospermia.

Among couples, male factors account for approximately 50% of infertility cases, with nonobstructive azoospermia (NOA) representing one of the most clinically common and severe categories of male infertility, affecting approximately 10-15% of patients. Currently, L-carnitine is clinically used to improve spermatogenesis by regulating Sertoli cell function. Multiple clinical trials have described the efficacy of L-carnitine in treating NOA. Notably, Sertoli cells rely on organic carnitine transporter 2 (OCTN2) for carnitine transport. However, it remains unknown whether OCTN2 expression is involved in the pathological process of NOA. To investigate the expression and function of OCTN2 in Sertoli cells from patients with NOA. Ten testicular tissue samples were collected, including five from a healthy group and five from a group of patients with NOA. Immunohistochemistry and immunofluorescence were used to detect the expression of OCTN2 in testicular tissue. Additionally, an Octn2-KO TM4 cell line (a mouse testicular Sertoli cell line) was constructed to explore the function of OCTN2 expression in Sertoli cells through transcriptomic sequencing, cell proliferation experiments, metabolomic analysis, and Western blot analysis. Compared with those of the healthy group, the immunohistochemistry results revealed a significant decrease in OCTN2 expression in the Sertoli cells of the NOA group. Further investigation through cell proliferation experiments revealed a reduction in the proliferative capacity of the Octn2-KO TM4 cell line. Transcriptomic sequencing and metabolomic data analysis revealed a decrease in autophagy in the Octn2-KO TM4 cell line. Western blot analysis subsequently verified the expression levels of autophagy-related proteins. In the Sertoli cells of NOA patients, decreased OCTN2 protein expression leads to decreased cell proliferation and autophagy abnormalities, which may play a crucial role in the spermatogenic dysfunction observed in NOA patients.

Prevalence of severe and moderate anthropometric failure among children in India, 1993-2021.

Though child anthropometric failure (CAF) is a persistent problem in India, previous studies have often neglected state-level variance and aggregated moderate and severe CAF categories. This study addresses this gap by examining moderate and severe malnutrition across India's states and union territories (UTs) from 1993 to 2021. Data of children under 2 years old from five waves of National Family Health Surveys, a representative cross-sectional survey of Indian households, were analysed. Outcomes included prevalence of moderate and severe stunting, underweight and wasting, as per the 2006 World Health Organization growth standards. Percentage prevalence and standardized absolute change (SAC) were calculated nationally and by region for each wave. From 1993 to 2021, there was a notable reduction in the nationwide prevalence of moderate stunting, underweight and wasting, with rates dropping from 20% to 16%, 23% to 18%, and 15% to 12%, respectively. Severe stunting and underweight declined considerably from 23% to 16% and 18% to 11%, respectively; severe wasting marginally increased from 8% to 9%. From 2016 to 2021 moderate underweight was noted to have the highest SAC across all regions, although 15 regions saw an increase in the prevalence of moderate underweight. In the 2016-2021 period, severe wasting has increased in 13 of the 36 regions. While there has been a nationwide reduction in most indicators of CAF since 1993, the rate and direction of change vary widely among states and UTs and between moderate and severe categories within each of the states and UTs. Understanding these patterns of change can direct context-specific interventions for improving child nutrition and health. A greater focus on reducing severe wasting, which has increased since 1993, is also crucial.

Enhancing Traffic Incident Management with Large Language Models: A Hybrid Machine Learning Approach for Severity Classification

Enhancing Traffic Incident Management with Large Language Models: A Hybrid Machine Learning Approach for Severity Classification

A few-shot diabetes foot ulcer image classification method based on deep ResNet and transfer learning

Diabetes foot ulcer (DFU) is one of the common complications of diabetes patients, which may lead to infection, necrosis and even amputation. Therefore, early diagnosis, classification of severity and related treatment are crucial for the patients. Current DFU classification methods often require experienced doctors to manually classify the severity, which is time-consuming and low accuracy. The objective of the study is to propose a few-shot DFU image classification method based on deep residual neural network and transfer learning. Considering the difficulty in obtaining clinical DFU images, it is a few-shot problem. Therefore, the methods include: (1) Data augmentation of the original DFU images by using geometric transformations and random noise; (2) Deep ResNet models selection based on different convolutional layers comparative experiments; (3) DFU classification model training with transfer learning by using the selected pre-trained ResNet model and fine tuning. To verify the proposed classification method, the experiments were performed with the original and augmented datasets by separating three classifications: zero grade, mild grade, severe grade. (1) The datasets were augmented from the original 146 to 3000 image datasets and the corresponding DFU classification’s average accuracy from 0.9167 to 0.9867; (2) Comparative experiments were conducted with ResNet18, ResNet34, ResNet50, ResNet101, ResNet152 by using 3000 image datasets, and the average accuracy/loss is 0.9325/0.2927, 0.9276/0.3234, 0.9901/0.1356, 0.9865/0.1427, 0.9790/0.1583 respectively; (3) Based on the augmented 3000 image datasets, it was achieved 0.9867 average accuracy with the DFU classification model, which was trained by the pre-trained ResNet50 and hyper-parameters. The experimental results indicated that the proposed few-shot DFU image classification method based on deep ResNet and transfer learning got very high accuracy, and it is expected to be suitable for low-cost and low-computational terminal equipment, aiming at helping clinical DFU classification efficiently and auxiliary diagnosis.

Enhanced Mastitis Severity Classification in Dairy Cows Using DNN and RF: A Study on PCA and Correlation-Based Feature Selection

Mastitis presents a critical challenge in dairy farming, significantly impacting both animal health and economic stability. This study advances mastitis severity classification in Holstein-Friesian cows through an interdisciplinary approach that integrates advanced machine learning methods within a veterinary context, shifting focus from traditional disease detection or diagnosis to refined severity classification, which is critical for aligning treatment strategies with disease progression and optimizing intervention timing. Clinical measurements from 1,886 Holstein-Friesian dairy cows, aged 3-4 years, across 21 parameters were analyzed. Both Deep Neural Networks (DNN) and Random Forests (RF) were utilized, with dimensionality reduction applied through Principal Component Analysis (PCA) and correlation-based feature selection to retain essential features and enhance computational efficiency. Key parameters in the RF and DNN models were carefully selected to ensure robust performance across diverse datasets. By categorizing mastitis into acute, acute with bloody milk, and peracute stages, this study addresses unique diagnostic challenges within veterinary medicine. The RF and DNN models achieved superior accuracy, ranging from 97% to over 98%, surpassing traditional diagnostic methods in precision, recall, and F1 metrics. These findings highlight the broader applicability of machine learning advancements beyond veterinary sciences, demonstrating the potential for interdisciplinary research across veterinary medicine, computer science, and engineering to drive innovation in automated diagnostics and resource management in dairy farming. The research was conducted using the MATLAB® Deep Learning App.

THE ASSOCIATION OF ANEMIA WITH SURVIVAL AMONG PEOPLE WITH HIV FOLLOWING ANTIRETROVIRAL INITIATION IN THE NA-ACCORD 2007-2016.

Anemia is an independent predictor of mortality, which may be utilized as a signal of deteriorating health. We estimated the association between anemia severity categories and mortality following the initiation of antiretroviral therapy (ART) among people with HIV (PWH) in North America. Within the NA-ACCORD, annual median hemoglobin measurements between 01/01/2007-12/31/2016 were categorized using World Health Organization criteria into mild (11.0-12.9g/dL men, 11.0-11.9g/dL women), moderate (8.0-10.9g/dL men/women) and severe (<8.0g/dL men/women) anemia. Discrete time-to-event analyses using complementary log-log link models estimated mortality hazards ratios adjusted for demographics, comorbidities, and HIV clinical markers with 95% confidence intervals for the association between anemia and mortality. Among 67,228 PWH contributing a total of 320,261 annual median hemoglobin measurements, 257,293 (80%) demonstrated no anemia, 44,041 (14%) mild, 18,259 (6%) moderate, and 668 (0.2%) severe anemia during follow-up. Mortality risk was 5.6-fold higher among PWH with (vs. without) anemia. The association was greater among males (aHR=5.8 [5.4, 6.2]) versus females (aHR=4.1 [3.2, 5.4]). Mortality risk was 3.8-fold higher among PWH with mild anemia, 13.7-fold higher with moderate anemia, and 34.5-fold higher with severe anemia (vs. no anemia). Median hemoglobin levels significantly declined within 4 years prior to death, with the maximum decrease the year prior to death. Macrocytic anemia was associated with an increased and microcytic anemia a decreased mortality risk (vs. normocytic anemia). Anemia among PWH who have initiated ART is an important predictive marker for mortality with macrocytic anemia having an increased and microcytic anemia a decreased association with mortality compared with normocytic anemia.

Correlation Analysis Approach Between Features and Motor Movement Stimulus for Stroke Severity Classification of EEG Signal Based on Time Domain, Frequency Domain, and Signal Decomposition Domain

The healing process of a stroke necessitates tools for measuring relevant parameters to facilitate monitoring, evaluation, and medical rehabilitation. Accurate parameter measures can be observed in stroke patients' severity to ascertain suitable interventions by identifying components pertinent to monitoring, evaluation, and medical rehabilitation. The components are derived from the observation collection process utilizing an EEG device, accompanied by a motor stimulus, to ensure the acquisition of EEG signals for monitoring, evaluation, and medical rehabilitation while preventing any loss of information during data collection. The acquired information encounters challenges due to the signal's unstable, nonlinear, and non-stationary characteristics, necessitating efforts to stabilize, render stationary, and linearize it through suitable signal processing and feature extraction techniques to achieve a pertinent feature composition. The subsequent difficulty is achieving the objectives of medical monitoring, evaluation, and rehabilitation, necessitating the correlation between EEG signal characteristics and motor movement stimuli, ensuring that the process adheres to appropriate parameter identification and scheduling per the established plan. In response to this difficulty, a correlation analysis methodology is established, incorporating normalcy tests, significance tests, and correlation analysis to ensure that the relevant factors for identifying stroke severity categorization patterns are precisely identified beforehand. The correlation analysis strategy employs raw data situations, preprocessing, feature extraction, feature selection, and correlation analysis for classification purposes. Our experimental findings indicate that the correlation analysis approach for assessing stroke severity classification patterns is evident in the Hajorth Complexity feature, utilizing the Shoulder motor movement stimulus and the SVM classification type, achieving an accuracy significant value of 98%. These findings confirm the efficacy of correlation analysis between EEG signal features and motor movement stimuli in identifying the optimal parameters within a reduced dimensional space to assess stroke severity effectively.

Effects of major trauma care organisation on mortality in a European level 1 trauma centre: A retrospective analysis of 2016-2023.

The centralisation of care for trauma patients in trauma centres, alongside the creation of inclusive trauma networks, has proven to reduce mortality. In Europe, such structured trauma programs and trauma networks are in development. To describe the aetiology and evolution of in-hospital mortality in a developing European level 1 trauma centre, to determine the early effect of trauma care reorganisation on mortality and to identify the areas for future investments in trauma care. This retrospective analysis included the calculation of the standardised mortality ratio (SMR), the time to in-hospital death and the cause of in-hospital death of all primary major trauma admissions to the Antwerp University Hospital from 2016 to 2023. A total of 1470 patients was included with a crude mortality of 16.4 %, a median Revised Injury Severity Classification II (RISC II) adjusted mortality of 1.47 %, and a SMR of 1.12. A limitation of care directive was registered for 18.1 % of the patients. The causes of in-hospital death were traumatic brain injury (TBI) in 60 %, haemorrhagic shock in 15 %, organ failure in 10 %, miscellaneous in 14 % and unknown in 1 %. Sixty percent died in the first 48 h of hospital admission (mainly due to TBI and haemorrhagic shock) and 27 % died after more than seven days (mainly due to organ failure and TBI). In 24 % of the deceased patients with severe TBI, a non-TBI related cause of death was found. Overall, the SMR showed a nonsignificant decreasing trend, with a significant decrease of the SMR in the highest risk group (RISCII > 75 %) and a nonsignificant increase in the lowest risk group (RISC II <15 %). The standardised mortality ratio declined over a period of 8 years, even though the SMR increased nonsignificantly in the lowest risk-adjusted mortality group. Future analysis of this subgroup could clarify whether this trend is due to an increase of limitation of care directives and if these deaths could have been prevented with improved trauma care. There might be opportunities to increase the survival of patients with severe TBI who have a non-TBI cause of death.

Grip Strength and Carpal Tunnel: What Interest in Management

Introduction: Carpal tunnel syndrome (CTS) is one of the most common musculoskeletal disorders of the upper extremity. Strong evidence supports operative treatment, in motor forms, regardless of technique, as superior to nonoperative treatment. Severity of motor forms of CTS depend of clinicals, and Electroneuromyography aspects. According to ENMG aspects, therapeutics decisions are difficults. Hook grip strength could early diagnosed decreasy strenght, and classified patients. Objectives: Our study aim to determine strenght grip characteristics in differents class of severity in CTS. Patients and Methods: We conducted a multicenter study including patients with unilateral CTS, on two phases. Weren’t included, patients with bilateral CTS and pre-existing functional limitations of upper limb. We classed patients in 3 groups of severity according to motor ENMG results. Hook grip strength measured in all patients by a dynamometer with a maximum strenght of 90 kg on both sides and Functional capacity evaluated by Functional dimension of Boston Crapal Tunel Questionary (FBCTQ). We classes strenght grip on three groups of severity according to differences of both sides. Results: We collected 33 patients. Median age was 48,33 ± 11,67 years. Sex-ratio was 0,27. CTS was severe in 54. 5% of patients. Grip strenght decreased in 81.8% of patients. Minor reduced grip strenght interested 45.5% of patients. FBCTQ score is less than 2/5 in 64% of patients. Functional prognosis factor in CTS were FBCTQS (P= 0.035), trans-carpal Tunel Velocity (p=0,025). Conclusion: Therapeutics decisions in CTS must include evaluation of Hook Grip strength and FBCTQS.

The value of hippocampal sub-region imaging features for the diagnosis and severity grading of ASD in children

BackgroundHippocampal structural changes in Autism Spectrum Disorder (ASD) are inconsistent. This study investigates hippocampal subregion changes in ASD patients to reveal intrinsic hippocampal anomalies. MethodsA retrospective study from Hainan Children’s Hospital database (2020–2023) included ASD patients and matched controls. We classified ASD participants based on severity, dividing all subjects into four groups: normal, mild, moderate, and severe. High-resolution T1-weighted MRI images were analyzed for hippocampal subregion segmentation and volume calculations using Freesurfer. Texture features were extracted via the Gray-Level Co-occurrence Matrix. The Receiver Operating Characteristic curve was used to evaluate seven random forest predictive models constructed from volume, subregion, and texture features, as well as their combinations following feature selection. ResultsThe study included 114 ASD patients (98 boys, 2–8 years; 16 girls, 2–6 years; 17 mild, 57 moderate, 40 severe) and 111 healthy controls (HCs). No significant differences in volumes were found between ASD patients and HCs (adjusted P-value >0.05). The seven random forest models showed that single volume and texture features performed poorly for ASD classification; however, integrating various feature types improved AUC values. Further selection of texture, subregion, and volume features enhanced AUC performance across normal and varying severity categories, demonstrating the potential value of specific subregions and integrated features in ASD diagnosis. ConclusionRandom forest models revealed that hippocampal volume, texture features, and subregion characteristics are crucial for diagnosing and assessing the severity of ASD. Integrating selected texture and subregion features optimized diagnostic efficacy, while combining texture, subregion, and volume features further improved severity grading effectiveness.

Severity Classification of Parkinson’s Disease via Synthesis of Energy Skeleton Images from Videos Produced in Uncontrolled Environments

Background/Objectives: Parkinson’s Disease is a prevalent neurodegenerative disorder affecting millions worldwide, primarily marked by motor and non-motor symptoms due to the degeneration of dopamine-producing neurons. Despite the absence of a cure, current treatments focus on symptom management, often relying on pharmacotherapy and surgical interventions. Early diagnosis remains a critical challenge, particularly in underserved areas, as existing diagnostic protocols lack standardization and accessibility. This paper proposes a novel framework for the diagnosis and severity classification of PD using video data captured in uncontrolled environments. Methods: Leveraging deep learning techniques, our approach synthesizes Skeleton Energy Images (SEIs) from gait sequences and employs three advanced models—a Convolutional Neural Network (CNN), a Residual Network (ResNet), and a Vision Transformer (ViT)—to analyze these images. Our methodology allows for the accurate detection of PD and differentiation of its severity without requiring specialized equipment or professional oversight. The dataset used consists of labeled videos capturing the early stages of the disease, facilitating the potential for timely intervention. Results: The four models performed very accurately during the training phase. In fact, an accuracy higher than 99% was achieved by the ViT and ResNet models. Moreover, a lesser accuracy of 90% was achieved by the CNN five-layer model. During the test phase, only the best-performing models from the training experiments were tested. The ResNet-18 model has achieved a 100% accuracy. However, the ViT and the CNN five-layer models have achieved, respectively, 99.96% and 96.40% test accuracy. Conclusions: The results demonstrate high accuracy, highlighting the framework’s capabilities, and in particular the effectiveness of the workflow used for generating the SEI images. Given the nature of the dataset used, the proposed framework stands to function as a cost-effective and accessible tool for early PD detection in various healthcare settings. This study contributes to the advancement of mobile health technologies, aiming to enhance early diagnosis and monitoring of Parkinson’s Disease.

Hubungan Diabetes Melitus Tipe 2 dengan Derajat Obstruksi Penyakit Paru Obstruktif Kronik (PPOK) di RS. Royal Prima Periode 2022-2023

Type 2 Diabetes Mellitus (T2DM) is one of the most common comorbidities in patients with Chronic Obstructive Pulmonary Disease (COPD). COPD itself can be a risk factor in the development of T2DM through systemic inflammatory mechanisms caused by continuous and progressive exposure to cigarette smoke over time. To investigate the relationship between diabetes mellitus and the severity of COPD obstruction based on demographic data, spirometry test results, and fasting blood glucose (FBG) test results. This study used a quantitative method with an observational descriptive design. Sampling was done using total sampling technique with a cross-sectional survey design and a retrospective approach using secondary data. Out of 31 study subjects diagnosed with COPD and T2DM, 83.9% (26 subjects) were male and 16.1% (5 subjects) were female. In terms of age distribution, the 40–59-year age group had the highest frequency, totaling 18 individuals (58.1%). In terms of occupation, self-employed individuals represented the largest group with 24 subjects (77.4%). For COPD severity, the highest frequency was found in the severe COPD category, with 15 subjects (48.4%). Based on the results of the study investigating the relationship between Type 2 Diabetes Mellitus and the severity of COPD obstruction, a significant relationship was found between T2DM and COPD obstruction severity when analyzed by gender, age, occupation, and the results of spirometry and fasting blood glucose tests.

Effectiveness of Guided Imagery on Level of Perceived Stress and Depression Among Sober Alcohol Dependents Admitted in De-Addiction Centers of Selected Areas

Introduction: Alcohol abuse poses significant health challenges, affecting individuals and families. Guided imagery therapy offers a promising approach to manage stress and emotions, potentially mitigating the impacts of alcoholism and depression. Methodology: This study employed a quasi-experimental, non-randomized control group design with a quantitative approach to assess the effectiveness of guided imagery therapy on perceived stress and depression in sober alcohol dependents at selected de-addiction centers. Baseline characteristics, including age, gender, education, family type, occupation, income, and duration of alcohol use, were collected using Tool I. Stress levels were measured with the 5-point Perceived Stress Scale (Tool II), while the 4-point Hamilton Depression Scale (Tool III) assessed depression. Written permission from de-addiction authorities and informed consent from participants were obtained. The sample included 60 sober alcohol dependents from Maharashtra, selected via non-probability purposive sampling. Test-retest reliability for stress and depression was confirmed, with scores of 0.89 and 0.90, respectively. Result: The study assessed the effectiveness of guided imagery therapy on perceived stress and depression among sober alcohol dependents using a quasi- experimental design. In the experimental group, pre-test data revealed that 60% experienced severe perceived stress, which decreased to 66.66% at moderate levels post-intervention, demonstrating significant improvement (t-value: 20.018). Conversely, the control group showed no change, with 80% remaining in the severe category pre- and post-test (t-value: 0.1054). Similarly, for depression, the experimental group reported 63.34% severe depression pre-test, reducing to 70% at moderate levels post-test (t-value: 7.907), while the control group exhibited no significant change. Analysis of associations with demographic variables revealed no significant correlations for age, education, family type, or occupation; however, a significant relationship was found between the duration of alcohol consumption and depression levels (chi-square: 8.948). Conclusion: Thus, guided imagery therapy effectively perceived stress and depression reduced among the experimental group.