XSIM: A structural similarity index measure optimized for MRI QSM.

PURPOSE: The structural similarity index measure (SSIM) has become a popular quality metric to evaluate QSM in a way that is closer to human perception than RMS error (RMSE). However, SSIM may overpenalize errors in diamagnetic tissues and underpenalize them in paramagnetic tissues, resulting in biasing. In addition, extreme artifacts may compress the dynamic range, resulting in unrealistically high SSIM scores (hacking). To overcome biasing and hacking, we propose XSIM: SSIM implemented in the native QSM range, and with internal parameters optimized for QSM. METHODS: We used forward simulations from a COSMOS ground-truth brain susceptibility map included in the 2016 QSM Reconstruction Challenge to investigate the effect of QSM reconstruction errors on the SSIM, XSIM, and RMSE metrics. We also used these metrics to optimize QSM reconstructions of the in vivo challenge data set. We repeated this experiment with the QSM abdominal phantom. To validate the use of XSIM instead of SSIM for QSM quality assessment across a range of different reconstruction techniques/algorithms, we analyzed the reconstructions submitted to the 2019 QSM Reconstruction Challenge 2.0. RESULTS: Our experiments confirmed the biasing and hacking effects on the SSIM metric applied to QSM. The XSIM metric was robust to those effects, penalizing the presence of streaking artifacts and reconstruction errors. Using XSIM to optimize QSM reconstruction regularization weights returned less overregularization than SSIM and RMSE. CONCLUSION: XSIM is recommended over traditional SSIM to evaluate QSM reconstructions against a known ground truth, as it avoids biasing and hacking effects and provides a larger dynamic range of scores.

Differences in Sleep Quality and Sleepiness among Veterinary Medical Students at Multiple Institutions Before and After the Pandemic Induced Transition to Online Learning.

Poor sleep health has been previously documented in veterinary medical students. However, it is not known how universal or widespread this problem is. This study evaluated Pittsburgh Sleep Quality Index (PSQI) and Epworth Sleepiness Scale (ESS) scores to measure sleep health among students at seven colleges of veterinary medicine in the United States (US). Inadvertently, the transition to online only learning due to the global COVID-19 pandemic was also captured. Veterinary students were found to have universally poor sleep quality and high daytime sleepiness. The transition to online only learning appeared to have little impact on sleep quality, but improved daytime sleepiness scores were observed. The findings suggest poor sleep health is common among veterinary medical students at multiple institutions in the US and that further investigation is necessary.

Determining the Mean Dermatology Life Quality Index of Skin of Color (SOC) Patients With Vitiligo: A South Asian Perspective.

BACKGROUND: Vitiligo has a special significance to the skin of colour (SOC) population because depigmentation is more obvious in the SOC population. As a result, vitiligo exerts a huge stigma on the affected individuals. The literature so far has reported on the health-related quality of life (QOL), including the dermatology life quality index (DLQI), and has demonstrated the profound effect of vitiligo on a patient's QOL. Those affected by vitiligo are found to have lower self-esteem when compared to the non-affected individuals. The impact on the SOC population is even graver and underrepresented. Thus, the objective of this study was to report the mean DLQI in vitiligo in SOC individuals. MATERIALS AND METHODS: This descriptive, cross-sectional study was performed in the dermatology outpatient department of a tertiary healthcare setup in Rawalpindi Pakistan. A total of 113 patients suffering from vitiligo, aged 15 to 65 years, both male and female, were included in the study. In all the cases, the DLQI score was noted. RESULTS:  With the age ranging from 15 to 65 years and a mean age of 34.96 ± 9.59 years, 70% (n=77) of the patients were between 15 to 40 years of age. The male-to-female ratio was 1:1.3, with male patients comprising 38.21% (n=47) and females being 61.79% (n=66). The mean DLQI score of the vitiligo patients in our cohort was 9.39 ± 6.35. CONCLUSION: This study concluded that the mean DLQI score was higher in female patients and in vitiligo universalis patients, compared to other variants of vitiligo. The SOC population affected with vitiligo is at a higher risk of having a decreased QOL and hence may need special attention with regard to quality health delivery services.

[Chemical Spatiotemporal Characteristics and Environmental Driving Factors of Groundwater in Hetao Irrigation Area].

To explore the chemical characteristics and environmental factors of groundwater in the Hetao Irrigation Area of Inner Mongolia, five irrigation fields, including UulanBuh, Jiefangzha, Yongji, Yichang, and Wulat, were selected as the research area. From 72 groundwater observation wells, a total of 216 groundwater samples were collected throughout three typical periods： the end of freeze-thaw （March）, the middle of irrigation （July）, and the end of autumn watering （November）. Comprehensive methods were utilized, such as statistical analysis, Piper three-line diagram, Gibbs diagram, ion ratio, and principal component analysis, to explore the changes in the groundwater chemical environment and the environmental driving factors of groundwater component formation. The groundwater drinking suitability was evaluated using the water quality index （WQI）, and the irrigation suitability was analyzed using the USSL and Wilcox plots. The results indicated that the groundwater in the research areas was generally saline, and the total anion and cation concentrations in each period in ascending order were as follows： late freeze-thaw stage, late autumn irrigation stage, and mid-irrigation stage, with Na+ and Cl- being the major contributing ions. The chemical type of groundwater was dominated by Cl-Na, followed by Cl·SO4-Ca·Mg and a coexistence with SO4-Ca·Mg, HCO3·Cl-Na, HCO3-Na, and HCO3-Ca·Mg. Based on WQI values, the shallow groundwater in Hetao Irrigation District was mainly classified as Class IV and Class V, and the quality was poor in general. According to the USSL diagram and Wilcox diagram, the comprehensive evaluation results showed that the salinity and sodium concentration of shallow groundwater in the irrigation area were generally high. A total of 80.6% of the water samples during the late freeze-thaw period, 76.1% during the mid-irrigation period, and 77.6% during the late autumn irrigation period lacked irrigation suitability. Two major controlling factors of groundwater chemical characteristics were present in the study area, namely, evaporation and rock weathering, and Na+ and Cl- mainly came from the dissolution and cation exchange of salt rocks. Agricultural irrigation and drought were the chief driving factors of groundwater chemical evolution in the Hetao Irrigation Area. The study provides technical support for optimizing agricultural management measures and a theoretical reference for rational utilization of groundwater resources in the Yellow River irrigation area of Inner Mongolia.

Analysis of body composition, functionality and muscle-specific strength of older women with obesity, sarcopenia and sarcopenic obesity: a cross-sectional study.

During aging, changes in body composition can result in sarcopenic obesity, which is a condition in which obesity occurs accompanied by the loss of muscle mass and strength caused by sarcopenia. Although the effects of obesity and sarcopenia on body composition are known, the muscle-specific strength in older women with sarcopenic obesity remains under-researched. The objective of this study was to evaluate community-dwelling older women for the absence or presence of obesity, sarcopenia and sarcopenic obesity and compare them in terms of body composition, functional physical performance and muscle-specific strength. One hundred and fifty-six older women (± 74 years) were evaluated for body composition using Dual X-ray Absorptiometry, handgrip strength with a Jamar dynamometer and functional performance using gait speed and timed up and go tests. The presence of obesity, sarcopenia and sarcopenic obesity was found in 32.7%, 15.4% and 25% of the sample, respectively. Comparing groups, community-dwelling older women with sarcopenic obesity exhibited poorer functional physical performance (TUG ± 14 s), and lower muscle-specific strength (± 1.18). Sarcopenic obesity was associated with muscle-specific strength (95% IC 0.016-0.241), and TUG (95% CI 1.001-1.137). These findings indicate that the combination of obesity and sarcopenia has a negative impact on skeletal muscle, reducing muscle-specific strength and physical performance in older women with more declines than either disease alone. Therefore, this comprehensive assessment gives useful information for incorporating muscle-specific strength into the diagnosis of sarcopenic obesity in the older people.

Exploring metabolic syndrome and dietary quality in Iranian adults: a cross-sectional study.

BACKGROUND: Metabolic syndrome (MetS) is a cluster of cardiovascular risk factors affecting a quarter of the global population, with diet playing a significant role in its progression. The aim of this study is to compare the effectiveness of the Dietary Diabetes Risk Reduction Score (DDRRS) and the Macronutrient Quality Index (MQI) scoring systems in assessing the diet-related risk of metabolic syndrome. METHODS: In this cross-sectional study, data from 7431 individuals aged between 30 and 70 years, obtained from the Mashhad Cohort Study, were utilized to evaluate the risk factors of metabolic syndrome. A valid semi-quantitative food frequency questionnaire was used to assess participants' dietary intake. The MQI was calculated based on carbohydrate, fat, and healthy protein components, while the DDRRS was also computed. Anthropometric measurements and blood samples were taken to determine the presence of metabolic syndrome. Logistic regression analyses were conducted to assess the association between MQI and DDRRS with metabolic syndrome and its components. RESULTS: According to the crude model, we observed lower odds of MetS in the highest quartile of DDRRS and MQI compared to the lowest quartile (P-trend < 0.001). This trend persisted in the fully adjusted models, revealing odds ratios of 0.399 (95% CI: 0.319-0.500) and 0.597 (95% CI: 0.476-0.749) for DDRRS and MQI, respectively. After controlling for all potential confounders, we observed lower odds of central obesity in the highest quartile of MQI (OR: 0.818, 95% CI: 0.676-0.989, P-trend = 0.027). Furthermore, we found that the odds of high triglyceride levels were lower in the highest quartile of DDRRS compared to the lowest quartile (OR: 0.633, 95% CI: 0.521, 0.770, P-trend < 0.001). CONCLUSION: In conclusion, our study indicates that greater adherence to both DDRRS and MQI is linked to a decreased risk of metabolic syndrome and its components. These findings hold significant implications for public health and the development of personalized nutrition strategies.

Enhancing local-scale groundwater quality predictions using advanced machine learning approaches.

Assessing groundwater quality typically involves labor-intensive, time-consuming, and costly laboratory tests, making real-time monitoring impractical, especially at the local level. Groundwater quality projections at the local scale using broad spatial datasets have been inaccurate due to variations in hydrogeology, human activities, industrial operations, groundwater extraction, and waste disposal. This study aims to identify the most dependable and resilient machine learning algorithms for forecasting groundwater quality at nearby monitoring locations by utilizing simple water quality metrics that can be quickly assessed without extensive sampling and laboratory testing. The Entropy-weighted Water Quality Index (EWQI) was calculated using a large spatial and temporal dataset (2014-2021) of 977 wells with parameters including pH, total hardness (TH), calcium (Ca2⁺), magnesium (Mg2⁺), sodium (Na⁺), potassium (K⁺), sulfate (SO42⁻), chloride (Cl⁻), nitrate (NO3⁻), total dissolved solids (TDS), and fluoride (F⁻). Further, similar parameters were also observed in 33 open wells at the three local monitoring sites from December 2022 to March 2023. The EWQI was predicted using a Random Forest (RF), eXtreme Gradient Boosting (XGB), and Deep Neural Network (DNN). The pH, TH, and TDS were used as input variables for EWQI predictions, as they can be easily measured using handheld probes or multi-parameters. The model performance was evaluated using R2, MAE, and RMSE. During the training stage, all three models predicted the EWQI with an R2 greater than 90%, with minimal errors when pH, TH, and TDS were input variables. To validate the models at a local scale, the EWQI was predicted at the village level (e.g., Antoli, Balapura, and Lapodiaya) using pH, TH, and TDS as input variables. The machine learning models were able to predict the EWQI very closely to the actual EWQI, with an R2 greater than 90%. It is also evident that the models could predict the EWQI using basic parameters that are easily measured, providing an overall idea of the water quality for a small area. Hence, these machine learning models could be useful for accurately representing groundwater quality, thereby avoiding the use of time-consuming and costly laboratory techniques.

Efficacy Evaluation of Chlorine Dioxide and Hypochlorous Acid as Sanitisers on Quality and Shelf Life of Atlantic Salmon (Salmo salar) Fillets.

Microbial contamination during seafood processing can often lead to a reduction in shelf life and the possibility of food-borne illnesses. Sanitisation with chlorine-based products during seafood processing is therefore sometimes undertaken. This study compared the effects of two sanitisers, chlorine dioxide (ClO2) and hypochlorous acid (HOCl) at their suggested concentration (5 ppm and 10 ppm; 50 ppm and 100 ppm respectively), on physical, chemical, and microbial qualities of Atlantic salmon (Salmo salar) fillets throughout 7 days of simulated retail display refrigeration. Parameters used for assessment included quality index (QI), drip loss, colour, texture, histology, total volatile base nitrogen (TVB-N), lipid oxidation (malonaldehyde, MDA), pH, and total viable count changes. Results indicated that whilst drip loss increased over the storage time, day 4 and 7 drip loss in both sanitisers decreased significantly compared with the control. There was a linear relationship (R > 0.70) between QI and storage time in all treatments, particularly in regard to skin brightness, flesh odour, and gaping parameters, but treatment differences were not present. Texture parameters including gumminess, chewiness, and hardness increased over time in the control whilst both sanitiser treatments seemed to provide protective effects against texture hardening during storage. The observed softening effects from the sanitiser treatments were aligned with microstructural and cytological changes in the histology results, as evidenced by a reduced fibre-fibre adhesion, myodigeneration, and an increase in interfibrillar space over storage time. Colour, especially chroma (C*), was shown to decrease over time in control, whereas insignificant protective effects were observed in both sanitiser treatments at day 7. Irrespective of treatment and storage time, MDA levels exceeded the acceptable limit on all days, whilst TVB-N levels were below the critical limit. Although pH was influenced by treatment and storage time, the pH was within the normal range. Microbiological results showed that with sanitiser addition, TVC was below the permissible level (106 CFU/g) until day 4 but ClO2 ice (5 ppm), ClO2 (10 ppm), and HOCl (100 ppm) treated fillets all exceeded the limit on day 7. The mixed results on the effect of sanitiser addition on fillet quality and shelf life suggested that further investigation on pathogen reduction, sanitiser introductory method, as well as testing the same treatments in low-fat fish models would be recommended.

Effectiveness of Medhasagar Rasa® in Combating Aging-Associated Mild Neurocognitive Disorder: An Open-Label, Exploratory, Interventional Clinical Trial.

INTRODUCTION: With the rising prevalence of neurocognitive disorders (NCDs) among the aging population, particularly in conditions like mild cognitive impairment (MCI), which often precedes dementia, there remains a significant gap in effective pharmacological interventions. This has generated interest in exploring alternative therapies to manage symptoms and enhance cognitive function in the aging population. The primary objective of this study was to evaluate the effect of Medhasagar Rasa® on cognitive functions, daily functioning, and quality of life in participants with aging-associated mild neurocognitive disorder using the Montreal Cognitive Assessment (MoCA) Scale, Ayurvedic Manasabhava Scale, and Brief Cognitive Rating Scale (BCRS). METHODS: This open-label, interventional study at Karnatak Lingayat Education (KLE) Ayurveda Hospital, Belagavi, Karnataka, involved 32 screened participants, with 30 completing the study. Participants aged 50-70 years with MoCA scores of 18-25 received Medhasagar Rasa (2 tablets at bedtime, provided by M/s. Shree Dhootapapeshwar Limited, Mumbai, India) for 60 days. Assessments occurred at baseline and every 15 days until day 60. RESULTS: Thirty participants were recruited for the study after screening, all of whom completed the study. The median total MoCA score at baseline (visit one) was 20, which significantly improved to 25 by visit five (day 60±3) (p<0.001), indicating enhanced cognitive performance. The BCRS scores also showed significant improvement, with the median score decreasing from 12 to 7.5 (p<0.001) over 60 days. Anxiety symptoms were significantly reduced, with Hamilton Anxiety Rating Scale (HAM-A) scores dropping from 14 to 7 (p<0.001), while the Pittsburgh Sleep Quality Index (PSQI) scores indicated improved sleep quality, reducing from 9.5 to 7 (p<0.001). The Ayurvedic Manasabhava Scale also demonstrated a significant reduction in intensity (14 to 6; p<0.001) and frequency (13.5 to 6; p<0.001). Clinical Global Impression (CGI) scores showed stable illness severity, sustained global improvement, and consistent therapeutic efficacy. No adverse events were reported, and vital parameters remained normal throughout the study. Compliance with the medication was over 80%, and no significant changes were observed in laboratory values. CONCLUSION: Medhasagar Rasa effectively enhanced cognitive functions and alleviated anxiety and sleep disturbances in aging-related mild neurocognitive disorder, offering a promising therapeutic option.

Sleep quality disparities in different pregnancy trimesters in low- and middle-income countries: a systematic review and meta-analysis.

INTRODUCTION: Sleep is a crucial determinant of maternal and fetal health, significantly impacting the well-being of both the mother and her developing fetus. Poor sleep quality, characterized by difficulties in falling asleep or staying asleep, can cause poor pregnancy outcome. Conversely, studies came with inconsistent result in the prevalence of poor sleep quality in different trimester of pregnancy. Therefore, this systematic review and meta-analysis study aimed to compare the prevalence of poor sleep quality in different trimesters. METHOD: A systematic review and meta-analysis were done on published studies. Electronic data base search was done from PubMed, Hinari, Medline and Google Scholar. Data were extracted with Excel and the analysis were done using STATA version 17. Publication bias was assessed both graphically and statistically. I-square test was used to identify heterogeneity. RESULT: In this meta-analysis, 38 studies that measured poor sleep quality using the Pittsburg Sleep Quality Index (PSQI ≥ 5) were included. The pooled prevalence of poor sleep quality was identified as 37.46% (95% CI: 29.26, 45.67) in the first trimester, 47.62% (95% CI: 42.23, 53.02) in the second trimester, and 60.05% (95% CI: 51.32, 68.78) in the third trimester. CONCLUSION: This study identified a significant discrepancy in the prevalence of poor sleep quality, which increases as gestational age advances. Therefore, this discrepancy should be addressed, and additional support should be provided to pregnant women to help them achieve adequate sleep, especially as gestational age advances.

Residual kidney function and sleep quality in hemodialysis patients.

Background: Sleep disorders frequently affect end-stage renal disease patients on dialysis. However, the relationship between sleep quality and residual kidney function is still unclear. Therefore, this study aimed to investigate this relationship. Methods: In this analytical cross-sectional study, 225 patients who were referred to dialysis centers were studied, and based on renal function, they were classified into two groups with and without residual kidney function. The study employed the Pittsburgh Sleep Quality Index questionnaire to evaluate sleep quality. Multiple linear regression was utilized to determine the factors affecting sleep quality with a significance level consideration at p<0.05. Results: The mean age of patients was 58.23 ± 13.50 years. 58.7% of patients were males. The problem of serious and very serious sleep in the Sleep latency and sleep duration has been more than other components. 72% of hemodialysis patients had poor sleep quality. In the multiple linear regression model, age (ß = 0.442, 95% CI: 0.096, 0.788), sex (ß = -0.847, 95% CI: -1.641, -0.054), Body mass index (ß = 0.153, 95% CI: 0.058, 0.249) and dialysis duration (ß = 0.097, 95% CI: 0.002, 0.192) were independently and significantly associated with sleep quality score. However, there was no statistically significant relationship between sleep quality and residual kidney function. Conclusion: In conclusion, poor sleep quality is very common in patients undergoing hemodialysis. Therefore, sleep disorders in hemodialysis patients should be considered as one of the most challenging problems by healthcare providers, and early diagnosis and intervention are essential to improve sleep quality.

Assessment and prediction of Water Quality Index (WQI) by seasonal key water parameters in a coastal city: application of machine learning models.

The Water Quality Index (WQI) provides comprehensive assessments in river systems; however, its calculation involves numerous water quality parameters, costly in sample collection and laboratory analysis. The study aimed to determine key water parameters and the most reliable models, considering seasonal variations in the water environment, to maximize the precision of WQI prediction by a minimal set of water parameters. Ten statistical or machine learning models were developed to predict the WQI over four seasons using water quality dataset collected in a coastal city adjacent to the Yellow Sea in China, based on which the key water parameters were identified and the variations were assessed by the Seasonal-Trend decomposition procedure based on Loess (STL). Results indicated that model performance generally improved with adding more input variables except Self-Organizing Map (SOM). Tree-based ensemble methods like Extreme Gradient Boosting (XGB) and Random Forest (RF) demonstrated the highest accuracy, particularly in winter. Nutrients (Ammonia Nitrogen (AN) and Total Phosphorus (TP)), Dissolved Oxygen (DO), and turbidity were determined as key water parameters, based on which, the prediction accuracy for Medium and Low grades was perfect while it was over 80% for the Good grade in spring and winter and dropped to around 70% in summer and autumn. Nutrient concentrations were higher at inland stations; however, it worsened at coastal stations, especially in summer. The study underscores the importance of reliable WQI prediction models in water quality assessment, especially when data is limited, which are crucial for managing water resources effectively.

Groundwater geochemistry using modified integrated water quality index (IWQI) and health indices with special emphasis on nitrates and heavy metals in southern parts of Tirupati, South India.

Groundwater is particularly vulnerable to pollution in places with a high population density and extensive human usage of the land, especially in southern parts of Tirupati, India. To assess this, 60 bore-well samples were obtained and assessed for physical specifications, ion chemistry, and heavy metals during the pre- and post-monsoon seasons 2022. The current investigation employed a modified integrated water quality index (IWQI), conventional graphical and human health risk assessment (HHRA) of nitrates and heavy metals to know the groundwater chemistry and its detrimental health effects on humans. The major ions were analyzed using American public health association (APHA) standards, whereas heavy metals were analyzed using inductively coupled plasma optical emission spectrometry (ICP-OES). Additionally, pH Redox Equilibrium and C (PHREEQC), a geochemical model written in C programming language was employed to determine the saturation indices of mineral facies and ArcGIS 10.3.1 was used for spatial distribution patterns of IWQI. Then, the HHRA of nitrates and heavy metals was performed using United States environmental protection agency (US EPA) guidelines. The noteworthy outcomes include elevated levels of Ca2+, Mg2+, Cl-, NO3-, Cu, Fe, Mn, and Pb, demonstrating rock-water interaction, silicate weathering, Ca-Mg-HCO3 followed by mixed water facies, dissolution/precipitation, reverse exchange, and anthropogenic contamination are the major controlling processes in groundwater of southern Tirupati, India. The modified IWQI reveals that most groundwater samples (38%) fall under the bad quality class, with (47%) in the poor quality class and only (15%) classified as medium quality class in pre- and post-monsoon seasons. Elevated IWQI were observed in all directions except in the east, which is suitable for drinking. Moreover, the major hazard quotient (HQ) and hazard index (HI) for nitrates (NO3-) and heavy metals like copper (Cu), iron (Fe), manganese (Mn), lead (Pb) and zinc (Zn) are above the critical value of 1, revealing potential risk to humans, especially infants, followed by children and adults, entailing the instantaneous implementation of proper remedial measures and stringent policies to reduce the risk associated with groundwater pollution in the southern parts of Tirupati.

Efficacy Analysis of Temporary Spinal Cord Stimulation in the Treatment of Refractory Postherpetic Neuralgia.

BACKGROUND: Spinal cord stimulation can be considered in PHN patients if conservative treatment is not effective. However, the long-term pain outcomes of temporary (7-14 days) spinal cord stimulation (tSCS) in refractory PHN patients with a course of more than 3 months have not been  documented. OBJECTIVES: To investigate the efficacy of tSCS as a treatment for refractory PHN. STUDY DESIGN: Retrospective study. SETTING: Pain Department in a university hospital. METHODS: A total of 52 patients with refractory PHN were treated with tSCS between March 2018 and February 2021. Their medical records were collected, and the patients were divided into 3 groups according to the course of their disease into the medium-term group, long-term group and ultra-long-term group. The changes in the numeric rating scale (NRS) scores, Pittsburgh sleep quality index (PSQI) responses, pain relief rate, postoperative efficiency and patients' use of analgesics were recorded before the operation, 3 days, 10 days, one month, 3 months, 6 months and 12 months after the operation. RESULTS: The average NRS scores, the maximum NRS scores and the PSQI scores at 3 days, 10 days, one month, 3 months, 6 months and 12 months after the operation were significantly lower than those before the operation (P < 0.05). The average NRS scores and the maximum NRS scores of all groups increased significantly from one month to 6 months compared to those at 10 days after the tSCS treatment, and they decreased significantly at 12 months compared with 6 months post-operation. The average NRS scores of the medium-term and long-term group were significantly lower than that of the ultra-long-term group at 1-3 months after the operation, and the maximum NRS scores at one month, 3 months and 12 months after the operation were also significantly lower in the medium-term and long-term group compared to the ultra-long-term group. The average PSQI scores at 1-12 months after the operation were not significantly higher than that at 10 days after the operation, but it decreased significantly at 12 months compared with 6 months after the operation. Among the 3 groups, the PSQI scores of the medium-term and long-term group were significantly lower than those of the ultra-long-term group at 6 months after the operation. The postoperative pain relief rate ranged from 41.51%-59.81%, and the total effective rate was 42.31%-69.23%, and there was no significant difference among the 3 groups. Some patients still needed analgesics at 12 months after the operation, but the number of patients who were taking medications post-operation was significantly lower than that before the operation. LIMITATIONS: This is a single-center retrospective study with the inability to completely control for variables. Additionally, the number of cases is small and the follow-up duration is short. CONCLUSION: tSCS can be used as a safe and effective method to relieve refractory PHN, and the curative effect is substantially higher in patients with a disease course of 3-12 months compared to that in patients with a course of more than 12 months.

Air quality index prediction with optimisation enabled deep learning model in IoT application.

The development of industrial and urban places caused air pollution, which has resulted in a variety of effects on individuals and the atmosphere over the years. The measurement of the air quality index (AQI) depends on various environmental situations, such as emissions, dispersions, and chemical reactions. This paper developed the Internet of Things (IoT)-based Deep Learning (DL) technique for predicting air quality. Initially, the IoT simulation is performed, where the nodes receive input data. The routing technique is used to identify the best route toward the Base station (BS). The proposed Tangent Two-Stage Algorithm (TTSA) is used in the routing mechanism. For AQI prediction, the time series data is transmitted to the BS. The Z-score normalisation is employed to neglect the unessential data. Furthermore, feature indicator extraction is employed to extract the relevant feature indicators. The Deep Feedforward Neural Network (DFNN) is used to predict air quality. Furthermore, the proposed Fractional Tangent Two-Stage Optimisation (FTTSA) is employed for the training process of DFNN. Moreover, metrics such as energy, time, and distance are used to evaluate the routing process, and superior results such as 0.979J, 0.025s and 0.196 m are obtained. Furthermore, the AQI is predicted by metrics like root mean square error (RMSE), R-squared (R2), mean square error (MSE), and mean absolute percentage error (MAPE), whereas the superior values such as 0.602, 0.598, 0.362, and 0.456 are attained.

Machine learning for air quality index (AQI) forecasting: shallow learning or deep learning?

In this study, several machine learning (ML) models consisting of shallow learning (SL) models (e.g., random forest (RF), K-nearest neighbor (KNN), weighted K-nearest neighbor (WKNN), support vector machine (SVM), artificial neural network (ANN), and deep learning (DL) models (e.g., long short-term memory (LSTM), gated recurrent unit (GRU), recurrent neural network (RNN), and convolutional neural network (CNN)) have been employed for predicting air pollution and its classification. The models were selected based on factors such as prediction accuracy, model generalization, model complexity, and training time. Our study focuses on analyzing and predicting the air quality index (AQI) using daily PM10 concentration as natural pollutants and nine meteorological parameters from March 2013 to February 2022 in Zabol. We also utilized the information gain (IG) method for feature selection. Several measures including accuracy, F1 score, precision, recall, and the area under the curve (AUC), are computed to assess model performance. This study demonstrates the efficacy of DL models, particularly CNN, in predicting the AQI with remarkable accuracy. Our findings reveal that all models effectively classify air quality levels, with an AUC of 0.95 for the good class in both DL and ANN models, significantly outperforming SL models. The AUC values for the hazardous and moderate classes of DL models were also impressive, at 0.90 and 0.83, respectively, underscoring their effectiveness in critical classifications. In terms of performance, CNN achieved an accuracy of 0.60, leading the models, while RF followed closely at 0.58. RNN, GRU, ANN, and SVM each reached an accuracy of 0.57, demonstrating a competitive edge. LSTM and WKNN recorded an accuracy of 0.55, and KNN was slightly lower at 0.53. These results highlight the superior capabilities of DL models in addressing complex air quality classifications, providing invaluable insights for policymakers. By leveraging these advanced techniques, stakeholders can implement more effective strategies to combat air pollution and safeguard public health. It is worth noting that irregular monitoring of air quality data may affect the robustness of our predictions, highlighting the need for more consistent data collection to ensure an accurate representation of pollution levels.

International dietary quality index and its association with diabetes in RaNCD cohort study.

Diabetes and its complications pose a significant threat to global health. Various factors contribute to the development of diabetes, with diet being an important trigger. The Dietary Quality Index-International (DQI-I) serves as an indicator of changes in diet and its association with chronic diseases, including diabetes. The aim of this study is to examine the association between DQI-I and diabetes in adults. Data from the first phase of the Ravansar Non-Communicable Disease Cohort Study (RaNCD) were used for this cross-sectional study. The study included individuals from western Iran aged between 35 and 65 years. The DQI-I was used to assess diet quality and the essential aspects of a healthy diet. Multiple logistic regression analyses were performed to compare DQI-I total score and diabetes. A total of 7,079 individuals were included, including 608 diabetic and 6,471 healthy individuals. The mean DQI-I score was 60.51 ± 8.47 in healthy individuals and 63.12 ± 8.64 in diabetics. The odds of developing diabetes were higher in individuals with a higher DQI-I (adjusted odds ratio: 1.49, 95% CI: 1.30-1.73). The variety was 13.43 ± 4.47 in diabetics and 12.59 ± 4.79 in healthy individuals. Adequacy was 33.23 ± 3.71 in diabetics and 33.79 ± 3.37 in healthy individuals. Moderation was 13.27 ± 6.05 in diabetics and 11.79 ± 5.47 in healthy individuals. The overall balance was 2.88 ± 2.21 in the healthy group and 2.61 ± 2.13 in the diabetics. The macronutrient ratio was 2.15 ± 1.88 in the healthy group and 2.04 ± 1.84 in the diabetics. The fatty acid ratio was 0.72 ± 1.29 in the healthy group and 0.56 ± 1.17 in the diabetic group. The overall balance score was higher in the healthy subjects. The DQI-I total score was higher in diabetics, indicating a positive association between diabetes and the DQI-I. Therefore, the importance of continuous dietary management and education of diabetic patients should be emphasized.

Sleep Quality, Daytime Sleepiness and Sleep Hygiene Among Youth in a Rural District in South India: A Cross-Sectional Study.

Background Adequate sleep is crucial for youth cognitive function, academic performance, and mental health. However, various factors, including academic pressure, technology use, and socio-cultural norms can significantly impact sleep patterns, particularly in rural settings. This cross-sectional study assessed sleep quality, daytime sleepiness prevalence, and sleep hygiene practices among youth in a rural South Indian district. We also investigated factors associated with these sleep parameters in this understudied population Methods This was a cross-sectional study among 852 young subjects who were assessed with a self-reported proforma of socio-demographic details, behavioural factors, Pittsburgh Sleep Quality Index and Epworth Sleepiness Scale. Regression model was created to analyse the predictor for sleep quality and daytime sleepiness with respect to sociodemographic variables, behavioural factors and sleep hygiene practices. Results Our study revealed that 49.4% (n=421) of participants exhibited poor sleep quality, while 29.5% (n=251) reported abnormal daytime sleepiness. The most prevalent sleep hygiene practices were reading in bed (68.5%, n=584) and pre-bedtime eating (56.22%, n=479). Multivariate analysis indicated that sleep quality was significantly associated with accommodation type, with increased odds for those in private accommodation (adjusted odds ratio (AOR) 1.29, 95% CI: 1.14-1.62) and hostels (AOR 3.79, 95% CI: 1.79-8.06). Additionally, eating in bed (AOR 1.42, 95% CI: 1.03-1.95) and pre-bedtime eating (AOR 1.41, 95% CI: 1.07-1.88) were also associated with poor sleep quality. Factors significantly associated with daytime sleepiness included younger age (AOR 0.84, 95% CI: 0.75-0.94), non-medical academic streams (AOR 1.94, 95% CI: 1.33-2.83), extensive internet usage (three or more hours) (AOR 1.87, 95% CI: 1.15-3.13), watching TV in bed (AOR 1.46, 95% CI: 1.06-1.99), writing (AOR 1.45, 95% CI: 1.02-2.06), and eating in bed (AOR 1.55, 95% CI: 1.09-2.21). Conclusion This study reveals a significant incidence of poor sleep quality and daytime drowsiness among young individuals residing in rural areas of South India. The results of our study emphasize significant connections between sleep disturbances and several modifiable aspects, such as the kind of accommodation, eating habits, and use of technology. The impact of eating behaviours, both in bed and before bedtime, on sleep quality and daytime sleepiness underscores the importance of proper sleep hygiene education. Furthermore, the relationship between extensive internet usage and daytime sleepiness points to the growing influence of digital technology on youth sleep patterns. These findings emphasize the need for comprehensive sleep health programs tailored to rural youth. Such initiatives should address environmental factors, promote healthy sleep hygiene practices, and provide guidance on balanced technology use. Additionally, the varying impact of academic streams on sleep parameters suggests that sleep health strategies may need to be customized for different educational contexts.

Hydrogeochemical evolution and heavy metal characterization of groundwater from southwestern, Nigeria: An integrated assessment using spatial, indexical, irrigation, chemometric, and health risk models.

This study examines the hydrogeochemical and heavy metal parameters of groundwater in Ojo District to determine its suitability for use, potential sources, and human health implications. Ten groundwater samples were assessed, and hydrogeochemical modelling was performed via the Aquachem software. The chemical ions were in the following order: EC > (107.78-448.65 µS/cm) > TDS (182.02-320.77 mg/l) > TH (46.22-182.45 mg/l) > pH (5.55-6.35); HCO3 - (64.13-125.82 mg/l) > Na+ (36.87-96.49 mg/l) > Ca2+ (47.65-58.88 mg/l) > SO4 2- (19.94-53.67) > NO3 - (15.55-44.25 mg/l) > Cl- (20.43-27.16 mg/l) > Mg2+ (11.09-16.87 mg/l) and K+ (2.55-7.86 mg/l). The concentrations of heavy metals in groundwater were in the range of: Fe (0.11-0.27 mg/l) > Mn (0.003-0.16 mg/l) > Ni (0.05-0.12 mg/l) > Zn (0.003-0.05 mg/l) > Pb (0.001-0.03 mg/l) > As (0.001-0.005 mg/l) > Cr (0.002-0.005 mg/l) > Cd (0.001-0.003 mg/l) and Cu (0.001-0.0002 mg/l), with Pb, Mn, and Ni exceeding their allowable limits. The Schoeller and Gibbs plots revealed that the major mechanisms controlling the aquifer groundwater in Ojo region are geological rock weathering and mineralization, with a minimal influence of saltwater intrusion. The piper trilinear diagram also revealed that none of the cation was dominant while the anions were strongly dominated by HCO3 - (weak acids). The hydrogeochemical facies which describes the geochemical characteristics of the groundwater were classified into 3 types; "Ca2+-Mg+-HCO3 - (65 %)", "mixing zones (30 %)", and "Na+-K+-Cl--HCO3 - (5 %)". The hydrogeochemical modelling revealed that the groundwater is characterized by forward cation exchange, while rock-water interactions (silicate dissolution) were heavily involved in the geochemical processes. The single pollution index showed that Pb, Ni, and Mn contributed significantly to contamination, and the multi-pollution indices showed that the groundwater was slightly-moderately polluted. The integrated groundwater quality index revealed that only 10 % were clean, 50 % were poor or moderately unclean, 30 % were highly unclean, and only 10 % were extremely unclean (unfit for utilization). The water pollution index showed that 70 % of the groundwater was good. The irrigation indices suggest that the groundwater would enhance soil quality and support plant growth. Multivariate analysis revealed that the groundwater is being influenced by geogenic factors and anthropogenic activities. The health risk assessment (Hazard Quotient and Hazard Index) showed that exposure of adults to the investigated groundwaters could result in noncarcinogenic adverse effects. The cancer risk values also exceeded the minimum limit (1.0 x 10-6) and thresholds (1.0 x 10-4) for adults, indicating the carcinogenic potential of the groundwater.

Enhanced vertical railway track quality index with dynamic responses from moving trains.

The conventional vertical track quality index (TQI) based on the standard deviation of longitudinal levels yields standardized railway track condition assessment. Nevertheless, its capability to identify problems is limited, particularly in the ballast and substructure layers when abrupt changes affect train-track interaction. Previous research shows that dynamic responses from moving trains via axle box acceleration (ABA) measurements can quantify abrupt changes in the vertical dynamic responses. Thus, this paper proposes a framework to design an enhanced vertical TQI, called EnVTQI, by integrating track longitudinal levels and dynamic responses from ABA measurements. First, measured ABA signals are processed to mitigate the influence of variation in measurement speed. Then, substructure and ballast-related features are extracted, including scale average wavelet power (SAWP) in the ranges 0.04 m-1 to 0.33 m-1 (substructure) and 1.25 m-1 to 2.50 m-1 (ballast). This enables identifying track conditions at different track layers. Finally, EnVTQI is determined by weight averaging between the conventional vertical TQI and the ABA features from moving trains. The performance of EnVTQI is evaluated based on 48 segments of a 200-m track on a Dutch railway line. The results indicate that EnVTQI helps to distinguish track segments that cause poor train-track interaction, which the conventional TQI does not indicate. EnVTQI can supplement the conventional TQI, improving the effectiveness of track maintenance decision-making.