Predictive modeling of perioperative patient deterioration: combining unanticipated ICU admissions and mortality for improved risk prediction.

OBJECTIVE: This paper presents a comprehensive analysis of perioperative patient deterioration by developing predictive models that evaluate unanticipated ICU admissions and in-hospital mortality both as distinct and combined outcomes. MATERIALS AND METHODS: With less than 1% of cases resulting in at least one of these outcomes, we investigated 98 features to identify their role in predicting patient deterioration, using univariate analyses. Additionally, multivariate analyses were performed by employing logistic regression (LR) with LASSO regularization. We also assessed classification models, including non-linear classifiers like Support Vector Machines, Random Forest, and XGBoost. RESULTS: During evaluation, careful attention was paid to the data imbalance therefore multiple evaluation metrics were used, which are less sensitive to imbalance. These metrics included the area under the receiver operating characteristics, precision-recall and kappa curves, and the precision, sensitivity, kappa, and F1-score. Combining unanticipated ICU admissions and mortality into a single outcome improved predictive performance overall. However, this led to reduced accuracy in predicting individual forms of deterioration, with LR showing the best performance for the combined prediction. DISCUSSION: The study underscores the significance of specific perioperative features in predicting patient deterioration, especially revealed by univariate analysis. Importantly, interpretable models like logistic regression outperformed complex classifiers, suggesting their practicality. Especially, when combined in an ensemble model for predicting multiple forms of deterioration. These findings were mostly limited by the large imbalance in data as post-operative deterioration is a rare occurrence. Future research should therefore focus on capturing more deterioration events and possibly extending validation to multi-center studies. CONCLUSIONS: This work demonstrates the potential for accurate prediction of perioperative patient deterioration, highlighting the importance of several perioperative features and the practicality of interpretable models like logistic regression, and ensemble models for the prediction of several outcome types. In future clinical practice these data-driven prediction models might form the basis for post-operative risk stratification by providing an evidence-based assessment of risk.

Exploring seasonal variability in water quality of Nyabarongo River in Rwanda via water quality indices and DPSIR modelling.

Understanding seasonal variations in water quality is crucial for effective management of freshwater rivers amidst changing environmental conditions. This study employed water quality index (WQI), metal index (MI), and pollution indices (PI) to comprehensively assess water quality and pollution levels in Nyabarongo River of Rwanda. A dynamic driver-pressure-state-impact-response model was used to identify factors influencing quality management. Over 4 years (2018-2021), 69 samples were collected on a monthly basis from each of the six monitoring stations across the Nyabarongo River throughout the four different seasons. Maximum WQI values were observed during dry long (52.90), dry short (21.478), long rain (93.66), and short rain (37.4) seasons, classified according to CCME 2001 guidelines. Ion concentrations exceeded WHO standards, with dominant ions being HCO 3 - and Mg 2 + . Variations in water quality were influenced by factors such as calcium bicarbonate dominance in dry seasons and sodium sulfate dominance in rainy seasons. Evaporation and precipitation processes primarily influenced ionic composition. Metal indices (MI) exhibited wide ranges: long dry (0.2-433.0), short dry (0.1-174.3), long rain (0.1-223.7), and short rain (0.3-252.5). The hazard index values for Cu2+, Mn4+, Zn2+, and Cr3+ were below 1, ranging from 8.89E - 08 to 7.68E - 07 for adults and 2.30E - 07 to 5.02E - 06 for children through oral ingestion, and from 6.68E - 10 to 5.07E - 07 for adults and 6.61E - 09 to 2.54E - 06 for children through dermal contact. With a total carcinogenic risk of less than 1 for both ingestion and dermal contact, indicating no significant health risks yet send strong signals to Governmental management of the Nyabarongo River. Overall water quality was classified as marginal in long dry, poor in short dry, good in long rain, and poor again in short rain seasons.

Multidimensional impact of sport types on the psychological well-being of student athletes: A multivariate investigation.

The correlation between sports participation and psychological well-being is well-documented, revealing a complex interplay influenced by competition level and cultural context. This is particularly relevant in Korea, where the university sports culture significantly impacts student life. This study evaluates how competitive versus non-competitive sports affect Korean university students' psychological well-being using a quantitative approach with SmartPLS 4 for multi-group analysis. Findings reveal that competitive sports significantly enhance mental toughness and stress management through structured coping mechanisms and robust social support, improving coping strategy effectiveness by 34 % compared to non-competitive sports. Conversely, participants in non-competitive sports experience greater general well-being with a 40 % higher use of informal support. These insights suggest that university sports programs could benefit from targeted interventions incorporating specific coping strategies and social support frameworks tailored to the competitive context. This research underscores the need for precise stress management techniques and resilience-building exercises in sports curricula to optimize psychological well-being across different sports environments in Korean universities.

CT evaluation of bowel wall enhancement in pneumatosis intestinalis: preventing non-therapeutic laparotomies.

PURPOSE: To evaluate the diagnostic performance of bowel wall enhancement for diagnosing concomitant bowel ischemia in patients with parietal pneumatosis (PI) diagnosed at abdominal CT. MATERIALS AND METHODS: From January 1, 2012 to December 31, 2021, 226 consecutive patients who presented with PI on abdominal CT from any bowel segment were included. Variables at the time of the CT were retrospectively extracted from medical charts. CT examinations were blindly analyzed by two independent radiologists. The third reader classified all disagreement of bowel enhancement in three categories: (1) normal bowel enhancement; (2) doubtful bowel wall enhancement; (3) absent bowel wall enhancement. Multivariable logistic regression analysis was performed. Concomitant bowel ischemia was defined as requirement of bowel resection specifically due to ischemic lesion in operated patients and death from bowel ischemia in non-operated patients. RESULTS: Overall, 78/226 (35%) patients had PI associated with concomitant bowel ischemia. At multivariate analysis, Only absence or doubtful bowel wall enhancement was associated with concomitant bowel ischemia (OR = 167.73 95%CI [23.39-4349.81], P < 0,001) and acute mesenteric ischemia associated with PP (OR = 67.94; 95%CI [5.18-3262.36], P < 0.009). Among the 82 patients who underwent a laparotomy for suspected bowel ischemia, rate of non-therapeutic laparotomy increased from 15/59 (25%), 2/6 (50%) and 16/17 (94%) when bowel wall enhancement was absent, doubtful and normal respectively. CONCLUSION: Absence of enhancement of the bowel wall is the primary feature associated with concomitant bowel ischemia. It should be carefully assessed when PI is detected to avoid non-therapeutic laparotomy.

MANOCCA: a robust and computationally efficient test of covariance in high-dimension multivariate omics data.

Multivariate analysis is becoming central in studies investigating high-throughput molecular data, yet, some important features of these data are seldom explored. Here, we present MANOCCA (Multivariate Analysis of Conditional CovAriance), a powerful method to test for the effect of a predictor on the covariance matrix of a multivariate outcome. The proposed test is by construction orthogonal to tests based on the mean and variance and is able to capture effects that are missed by both approaches. We first compare the performances of MANOCCA with existing correlation-based methods and show that MANOCCA is the only test correctly calibrated in simulation mimicking omics data. We then investigate the impact of reducing the dimensionality of the data using principal component analysis when the sample size is smaller than the number of pairwise covariance terms analysed. We show that, in many realistic scenarios, the maximum power can be achieved with a limited number of components. Finally, we apply MANOCCA to 1000 healthy individuals from the Milieu Interieur cohort, to assess the effect of health, lifestyle and genetic factors on the covariance of two sets of phenotypes, blood biomarkers and flow cytometry-based immune phenotypes. Our analyses identify significant associations between multiple factors and the covariance of both omics data.

Utilizing machine learning algorithms for precise discrimination of glycosuria in fluorescence spectroscopic data.

Fluorescence spectroscopy coupled with a random forest machine learning algorithm offers a promising non-invasive approach for diagnosing glycosuria, a condition characterized by excess sugar in the urine of diabetic patients. This study investigated the ability of this method to differentiate between diabetic and healthy control urine samples. Fluorescent spectra were captured from urine samples using a Xenon arc lamp emitting light within the 200 to 950 nm wavelength range, with consistent fluorescence emission observed at 450 nm under an excitation wavelength of 370 nm. Healthy control samples were also analyzed within the same spectral range for comparison. To distinguish spectral differences between healthy and infected samples, the random forest (RF) and K-Nearest Neighbors (KNN) machine learning algorithms have been employed. These algorithms automatically recognize spectral patterns associated with diabetes, enabling the prediction of unknown classifications based on established samples. Principal component analysis (PCA) was utilized for dimensionality reduction before feeding the data to RF and KNN for classification. The model's classification performance was evaluated using 10-fold cross-validation, resulting in the proposed RF-based model achieving accuracy of 96 %, specificity of 100 %, sensitivity of 93 %, and precision of 100 %. These results suggest that the proposed method holds promise for a more convenient and potentially more accurate method for diagnosing glycosuria in diabetic patients.

Spectroscopy-based chemometrics combined machine learning modeling predicts cashew foliar macro- and micronutrients.

Precision nutrient management in orchard crops needs precise, accurate, and real-time information on the plant's nutritional status. This is limited by the fact that it requires extensive leaf sampling and chemical analysis when it is to be done over more extensive areas like field- or landscape scale. Thus, rapid, reliable, and repeatable means of nutrient estimations are needed. In this context, lab-based remote sensing or spectroscopy has been explored in the current study to predict the foliar nutritional status of the cashew crop. Novel spectral indices (normalized difference and simple ratio), chemometric modeling, and partial least square regression (PLSR) combined machine learning modeling of the visible near-infrared hyperspectral data were employed to predict macro- and micronutrients content of the cashew leaves. The full dataset was divided into calibration (70 % of the full dataset) and validation (30 % of the full dataset) datasets. An independent validation dataset was used for the validation of the algorithms tested. The approach of spectral indices yielded very poor and unreliable predictions for all eleven nutrients. Among the chemometric models tested, the performance of the PLSR was the best, but still, the predictions were not acceptable. The PLSR combined machine learning modeling approach yielded acceptable to excellent predictions for all the nutrients except sulphur and copper. The best predictions were observed when PLSR was combined with Cubist for nitrogen, phosphorus, potassium, manganese, and zinc; support vector machine regression for calcium, magnesium, iron, copper, and boron; elastic net for sulphur. The current study showed hyperspectral remote sensing-based models could be employed for non-destructive and rapid estimation of cashew leaf macro- and micro-nutrients. The developed approach is suggested to employ within the operational workflows for site-specific and precision nutrient management of the cashew orchards.

Effects of TCM external scalding therapy on spleen-stomach deficiency cold stomachache and inflammatory indexes.

OBJECTIVE: To determine the efficacy and safety of traditional Chinese medicine (TCM) external scalding therapy on spleen-stomach deficiency cold stomachache. METHODS: The medical records of 98 patients with spleen-stomach deficiency cold stomachache treated in the Affiliated Hospital of Jiangnan University from January 2019 to January 2020 were collected and analyzed retrospectively. Among them, 52 patients treated with western medicine were assigned to the control group, while the other 46 patients treated additionally with TCM external scalding therapy were assigned to the observation group. The two groups were compared in terms of serum gastrin (GAS), inflammatory factors and visual analogue scale (VAS) score, adverse reaction rate and symptom remission time. RESULTS: After treatment, the observation group showed a significantly lower GAS level than the control group (P<0.05), along with significantly lower serum levels of tumor necrosis factor-α (TNF-α), interleukin-1ß (IL-1ß) and interleukin-6 (IL-6) than the control group (all P<0.05). The observation group demonstrated significantly lower VAS score than the control group (P<0.05). The observation group experienced notably shorter remission time of dull epigastric pain, epigastric distension, fatigue and belching and acid reflux than the control group (all P<0.05), and a significantly lower incidence of adverse reactions was found in the observation group than that in the control group (P<0.05). Multivariate analysis revealed that history of alcoholism and treatment method were independent risk factors affecting patient outcomes (all P<0.05). CONCLUSION: TCM external scalding therapy has shown effectiveness in treating spleen-stomach deficiency cold stomachache. It alleviates stomachache symptoms and also reduces the occurrence of adverse reactions and inflammation, holding great potential for widespread adoption in clinical practice.

EXPRESS: Quasi-Non-Destructive Estimation of Concrete Compression Strength Using Laser-Induced Breakdown Spectroscopy and Multivariate Analysis.

Micro- and non-destructive methods of estimating compressive strength are useful for diagnosing the degradation of reinforced structures. The velocity of waves propagating through concrete can be measured using conventional non-destructive methods; however, the propagation path of waves varies depending on the distribution of coarse aggregate, resulting in variations in velocity at different measurement points. To address this issue, a method based on laser-induced breakdown spectroscopy (LIBS) and multivariate analysis was developed in this study for estimating the compressive strength of concrete non-destructively, ensuring the non-influence of the coarse aggregate spatial distribution. The method is based on the correlation between the emission intensity of the spectrum and the hardness of the object to be measured. Principal component analysis (PCA) and partial least squares regression (PLSR) were used to extract the mortar spectrum, which determines the compressive strength of concrete, from a mixture of aggregate and mortar spectra. The compressive strength estimated based on the proposed method was consistent with the values obtained from the compressive strength test, which indicates the possibility of using multi-variable analysis to estimate the compressive strength of concrete. Furthermore, the proposed method enabled on-site measurements through a simple experimental setup and insensitivity to spectral noise offered by partial least-squares regression.

Quality Evaluation of Lonicerae Flos Produced in Southwest China Based on HPLC Analysis and Antioxidant Activity.

Lonicera macranthoides, the main source of traditional Chinese medicine Lonicerae Flos, is extensively cultivated in Southwest China. However, the quality of L. macranthoides produced in this region significantly varies due to its wide distribution and various cultivation breeds. Herein, 50 Lonicerae Flos samples derived from different breeds of L. macranthoides cultivated in Southwest China were collected for quality evaluation. Six organic acids and three saponin compounds were quantitatively analyzed using HPLC. Furthermore, the antioxidant activity of a portion of samples was conducted with 2,2'-Azinobis-(3-ethylbenzthiazoline-6-sulphonate) (ABTS) and 1,1-diphenyl-2-picryl-hydrazyl (DPPH) radical scavenging experiments. According to the quantitative results, all samples met the quality standards outlined in the Chinese Pharmacopoeia. The samples from Guizhou, whether derived from unopened or open wild-type breeds, exhibited high quality, while the wild-type samples showed relatively significant fluctuation in quality. The samples from Chongqing and Hunan demonstrated similar quality, whereas those from Sichuan exhibited relatively lower quality. These samples demonstrated significant abilities in clearing ABTS and DPPH radicals. The relationship between HPLC chromatograms and antioxidant activity, as elucidated by multivariate analysis, indicated that chlorogenic acid, isochlorogenic acid A, isochlorogenic acid B, and isochlorogenic acid C are active components and can serve as Q-markers for quality evaluation.

Homo- and Heterogeneous Benzyl Alcohol Catalytic Oxidation Promoted by Mononuclear Copper(II) Complexes: The Influence of the Ligand upon Product Conversion.

The catalytic properties of three copper complexes, [Cu(en)2](ClO4)2 (1), [Cu(amp)2](ClO4)2, (2) and [Cu(bpy)2](ClO4)2 (3) (where en = ethylenediamine, amp = 2-aminomethylpyridine and bpy = 2,2'-bipyridine), were explored upon the oxidation of benzyl alcohol (BnOH). Maximized conversions of the substrates to their respective products were obtained using a multivariate analysis approach, a powerful tool that allowed multiple variables to be optimized simultaneously, thus creating a more economical, fast and effective technique. Considering the studies in a fluid solution (homogeneous), all complexes strongly depended on the amount of the oxidizing agent (H2O2), followed by the catalyst load. In contrast, time seemed to be statistically less relevant for complexes 1 and 3 and not relevant for 2. All complexes showed high selectivity in their optimized conditions, and only benzaldehyde (BA) was obtained as a viable product. Quantitatively, the catalytic activity observed was 3 > 2 > 1, which is related to the π-acceptor character of the ligands employed in the study. Density functional theory (DFT) studies could corroborate this feature by correlating the geometric index for square pyramid Cu(II)-OOH species, which should be generated in the solution during the catalytic process. Complex 3 was successfully immobilized in silica-coated magnetic nanoparticles (Fe3O4@SiO2), and its oxidative activity was evaluated through heterogenous catalysis assays. Substrate conversion promoted by 3-Fe3O4@SiO2 generated only BA as a viable product, and the supported catalyst's recyclability was proven. Reduced catalytic conversions in the presence of the radical scavenger (2,2,6,6-tetrametil-piperidi-1-nil)oxil (TEMPO) indicate that radical and non-radical mechanisms are involved.

Linear and Non-Linear Modelling Methods for a Gas Sensor Array Developed for Process Control Applications.

New process developments linked to Power to X (energy storage or energy conversion to another form of energy) require tools to perform process monitoring. The main gases involved in these types of processes are H2, CO, CH4, and CO2. Because of the non-selectivity of the sensors, a multi-sensor matrix has been built in this work based on commercial sensors having very different transduction principles, and, therefore, providing richer information. To treat the data provided by the sensor array and extract gas mixture composition (nature and concentration), linear (Multi Linear Regression-Ordinary Least Square "MLR-OLS" and Multi Linear Regression-Partial Least Square "MLR-PLS") and non-linear (Artificial Neural Network "ANN") models have been built. The MLR-OLS model was disqualified during the training phase since it did not show good results even in the training phase, which could not lead to effective predictions during the validation phase. Then, the performances of MLR-PLS and ANN were evaluated with validation data. Good concentration predictions were obtained in both cases for all the involved analytes. However, in the case of methane, better prediction performances were obtained with ANN, which is consistent with the fact that the MOX sensor's response to CH4 is logarithmic, whereas only linear sensor responses were obtained for the other analytes. Finally, prediction tests performed on one-year aged sensor platforms revealed that PLS model predictions on aged platforms mainly suffered from concentration offsets and that ANN predictions mainly suffered from a drop of sensitivity.

Lipids balance as a spectroscopy marker of diabetes. Analysis of FTIR spectra by 2D correlation and machine learning analyses.

The number of people suffering from type 2 diabetes has rapidly increased. Taking into account, that elevated intracellular lipid concentrations, as well as their metabolism, are correlated with diminished insulin sensitivity, in this study we would like to show lipids spectroscopy markers of diabetes. For this purpose, serum collected from rats (animal model of diabetes) was analyzed using Fourier Transformed Infrared-Attenuated Total Reflection (FTIR-ATR) spectroscopy. Analyzed spectra showed that rats with diabetes presented higher concentration of phospholipids and cholesterol in comparison with non-diabetic rats. Moreover, the analysis of second (IInd) derivative spectra showed no structural changes in lipids. Machine learning methods showed higher accuracy for IInd derivative spectra (from 65 % to 89 %) than for absorbance FTIR spectra (53-65 %). Moreover, it was possible to identify significant wavelength intervals from IInd derivative spectra using random forest-based feature selection algorithm, which further increased the accuracy of the classification (up to 92 % for phospholipid region). Moreover decision tree based on the selected features showed, that peaks at 1016 cm-1 and 2936 cm-1 can be good candidates of lipids marker of diabetes.

Predictions of biorelevant solubility change during dispersion and digestion of lipid-based formulations.

Computational approaches are increasingly explored in development of drug products, including the development of lipid-based formulations (LBFs), to assess their feasibility for achieving adequate oral absorption at an early stage. This study investigated the use of computational pharmaceutics approaches to predict solubility changes of poorly soluble drugs during dispersion and digestion in biorelevant media. Concentrations of 30 poorly water-soluble drugs were determined pre- and post-digestion with in-line UV probes using the MicroDISS Profiler™. Generally, cationic drugs displayed higher drug concentrations post-digestion, whereas for non-ionized drugs there was no discernible trend between drug concentration in dispersed and digested phase. In the case of anionic drugs there tended to be a decrease or no change in the drug concentration post-digestion. Partial least squares modelling was used to identify the molecular descriptors and drug properties which predict changes in solubility ratio in long-chain LBF pre-digestion (R2 of calibration = 0.80, Q2 of validation = 0.64) and post-digestion (R2 of calibration = 0.76, Q2 of validation = 0.72). Furthermore, multiple linear regression equations were developed to facilitate prediction of the solubility ratio pre- and post-digestion. Applying three molecular descriptors (melting point, LogD, and number of aromatic rings) these equations showed good predictivity (pre-digestion R2 = 0.70, and post-digestion R2 = 0.68). The model developed will support a computationally guided LBF strategy for emerging poorly water-soluble drugs by predicting biorelevant solubility changes during dispersion and digestion. This facilitates a more data-informed developability decision making and subsequently facilitates a more efficient use of formulation screening resources.

Classification of Fashion Models' Walking Styles Using Publicly Available Data, Pose Detection Technology, and Multivariate Analysis: From Past to Current Trendy Walking Styles.

Understanding past and current trends is crucial in the fashion industry to forecast future market demands. This study quantifies and reports the characteristics of the trendy walking styles of fashion models during real-world runway performances using three cutting-edge technologies: (a) publicly available video resources, (b) human pose detection technology, and (c) multivariate human-movement analysis techniques. The skeletal coordinates of the whole body during one gait cycle, extracted from publicly available video resources of 69 fashion models, underwent principal component analysis to reduce the dimensionality of the data. Then, hierarchical cluster analysis was used to classify the data. The results revealed that (1) the gaits of the fashion models analyzed in this study could be classified into five clusters, (2) there were significant differences in the median years in which the shows were held between the clusters, and (3) reconstructed stick-figure animations representing the walking styles of each cluster indicate that an exaggerated leg-crossing gait has become less common over recent years. Accordingly, we concluded that the level of leg crossing while walking is one of the major changes in trendy walking styles, from the past to the present, directed by the world's leading brands.

Long-term success and influencing factors of regenerative surgery for intra-bony defects: A retrospective cohort study.

BACKGROUND: The composite outcome measure (COM) more comprehensively assesses the clinical efficacy of regenerative surgery than a single probing measurement. We aimed to assess long-term success defined by the COM (clinical attachment level [CAL] gain of ≥3 mm and postsurgery probing pocket depth [PPD] ≤ 4 mm) and influencing factors of regenerative surgery using bone substitutes and resorbable collagen membrane (RM) for intra-bony defects (IBDs). METHODS: We retrospectively collected data from patients who underwent regenerative surgery using deproteinized bovine bone mineral (DBBM) and RM for IBDs. CAL and PPD values were compared at baseline (preoperative), 1 year (short-term), and at the last follow-up (5-10 years). Multivariate logistic regressions were performed to identify factors influencing COM-based long-term success. RESULTS: Eighty-one defects in 75 teeth of 33 patients who completed follow-up (6.5 ± 1.4 years) were included. One tooth was lost. All defects with complete follow-up exhibited long-term average CAL gain (3.00 ± 2.00 mm, 95% confidence interval [CI]: 2.56-3.44 mm, p < 0.001) and PPD reduction (2.06 ± 1.91 mm, 95% CI: 1.64-2.49 mm, p < 0.001). Long-term success was achieved in 38.8% of IBDs. CAL and PPD values were comparable between 1 year and the last follow-up. Logistic regression analyses revealed that male sex (odds ratio [OR] = 0.23, 95% CI: 0.07-0.75) and bleeding on probing (BOP) during supportive periodontal therapy (OR = 0.96, 95% CI: 0.94-0.99) were risk factors for long-term success. CONCLUSIONS: Regenerative surgery with DBBM and RM for IBDs can achieve some degree of long-term success defined by COM. However, within this study's limitations, male sex and higher BOP incidence postoperatively are negatively associated with optimal long-term success. CLINICAL TRIAL NUMBER: ChiCTR2300069016.

Limbic-predominant age-related TDP-43 encephalopathy in the oldest old: a population-based study.

Population-based cohort studies are essential for understanding the pathological basis of dementia in older populations. Previous studies have shown that limbic-predominant age-related TDP-43 encephalopathy neuropathological change (LATE-NC) increases with age, but there have been only a few studies, which have investigated this entity in a population-based setting. Here we studied the frequency of LATE-NC and its associations with other brain pathologies and cognition in a population aged ≥ 85 years. The population-based Vantaa 85+ study cohort includes all 601 individuals aged ≥ 85 years who were living in Vantaa, Finland in 1991. A neuropathological examination was performed on 304 subjects (50.5%) and LATE-NC staging was possible in 295 of those. Dementia status and Mini-Mental State Examination (MMSE) scores were defined in the baseline study and 3 follow-ups (1994-99). The LATE-NC stages were determined based on TDP-43 immunohistochemistry, according to recently updated recommendations. Arteriolosclerosis was digitally assessed by calculating the average sclerotic index of five random small arterioles in amygdala and hippocampal regions, and frontal white matter. The association of LATE-NC with arteriolosclerosis and previously determined neuropathological variables including Alzheimer's disease neuropathological change (ADNC), Lewy-related pathology (LRP), hippocampal sclerosis (HS), and cerebral amyloid angiopathy (CAA), and cognitive variables were analysed by Fisher's exact test, linear and logistic regression (univariate and multivariate) models. LATE-NC was found in 189 of 295 subjects (64.1%). Stage 2 was the most common (28.5%) and stage 3 the second most common (12.9%), whereas stages 1a, 1b and 1c were less common (9.5%, 5.1% and 8.1%, respectively). Stages 1a (P < 0.01), 2 (P < 0.001) and 3 (P < 0.001) were significantly associated with dementia and lower MMSE scores. LATE-NC was associated with ADNC (P < 0.001), HS (P < 0.001), diffuse neocortical LRP (P < 0.002), and arteriolosclerosis in amygdala (P < 0.02). In most cases LATE-NC occurred in combination alongside other neuropathological changes. There were only six subjects with dementia who had LATE-NC without high levels of ADNC or LRP (2% of the cohort, 3% of the cases with dementia), and five of these had HS. In all multivariate models, LATE-NC was among the strongest independent predictors of dementia. When LATE-NC and ADNC were assessed in a multivariate model without other dementia-associated pathologies, the attributable risk was higher for LATE-NC than ADNC (24.2% vs. 18.6%). This population-based study provides evidence that LATE-NC is very common and one of the most significant determinants of dementia in the general late-life aged population.

Combustion-Derived Pollutants Linked with Kidney Disease in Low-Lying Flood-Affected Areas in the Balkans.

Tens of thousands of people in southern Europe suffer from Balkan endemic nephropathy (BEN), and four times as many are at risk. Incidental ingestion of aristolochic acids (AAs), stemming from the ubiquitousAristolochia clematitis(birthwort) weed in the region, leads to DNA adduct-induced toxicity in kidney cells, the primary cause of BEN. Numerous cofactors, including toxic organics and metals, have been investigated, but all have shown small contributions to the overall BEN relative to non-BEN village distribution gradients. Here, we reveal that combustion-derived pollutants from wood and coal burning in Serbia also contaminate arable soil and test as plausible causative factors of BEN. Using a GC-MS screening method, biomass-burning-derived furfural and coal-burning-derived medium-chain alkanes were detected in soil samples from BEN endemic areas levels at up to 63-times and 14-times higher, respectively, than in nonendemic areas. Significantly higher amounts were also detected in colocated wheat grains. Coexposure studies with cultured kidney cells showed that these pollutants enhance DNA adduct formation by AA, - the cause of AA nephrotoxicity and carcinogenicity. With the coincidence of birthwort-derived AAs and the widespread practice of biomass and coal burning for household cooking and heating purposes and agricultural burning in rural low-lying flood-affected areas in the Balkans, these results implicate combustion-derived pollutants in promoting the development of BEN.

Amino acid profile behavior during the fermentation of Criollo cocoa beans.

The study investigated the behavior of seventeen amino acids during spontaneous (SF) and starter culture (SC) fermentation of Criollo cocoa beans from Copallín, Guadalupe and Tolopampa, Amazonas-Peru. For this purpose, liquid chromatography (UHPLC) was used to quantify amino acids. Multivariate analysis was used to differentiate the phases of the fermentation process. The percentage of essential amino acids during SC fermentation (63.4%) was higher than SF (61.8%); it was observed that the starter culture accelerated their presence and increased their concentration during the fermentation process. The multivariate analysis identified a first stage (day 0 to day 2), characterized by a low content of amino acids that increased due to protein hydrolysis. The study showed that adding the starter culture (Saccharomyces cerevisiae) to the fermentation mass increased the concentration of essential amino acids (63.0%) compared to the spontaneous process (61.8%). Moreover, this addition reduced the fermentation time (3-4 days less), demonstrating that the fermentation process with a starter culture allows obtaining a better profile of amino acids precursors of flavor and aroma.

Nomogram including renal pelvic pressure to predict the occurrence of urosepsis following percutaneous nephrolithotomy: a dual center retrospective study of 1,448 patients.

Background: Urosepsis is a serious complication after percutaneous nephrolithotomy (PCNL). This study aimed to develop and validate a nomogram model that can effectively predict urosepsis following PCNL. Methods: A total of 839 patients who underwent PCNL at General Hospital of Southern Theater Command from January 2018 to January 2023 and a total of 609 patients who underwent PCNL at Guangdong Second Provincial General Hospital from January 2020 to January 2023 were retrospectively analyzed in this study. The center with 839 patients was used to develop the model, and another center with 609 patients was used as an external validation group. Multivariate analysis was used to determine the optimal variables. The validation of the nomogram was assessed using the receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA). Results: Urosepsis was observed in 47 (5.6%) and 33 (5.4%) patients in the two centers. Four variables were selected to establish the nomogram through multivariate analysis, including operative time [P<0.001, odds ratio (OR): 1.035, 95% confidence interval (CI): 1.019-1.051], accumulated time of renal pelvic pressure ≥30 mmHg (0 vs. 0-60 s, P=0.011, OR: 3.180, 95% CI: 1.300-7.780; 0-60 vs. ≥60 s, P<0.001, OR: 6.389, 95% CI: 2.603-15.685), bladder urine culture (P<0.001, OR: 6.045, 95% CI: 2.454-14.891) and hydronephrosis (none or light vs. moderate, P=0.003, OR: 3.403, 95% CI: 1.509-7.674; moderate vs. several, P=0.002, OR: 4.704, 95% CI: 1.786-12.391). The calibration results showed that the model was well calibrated and ROC curve demonstrated excellent discrimination of the nomogram. In addition, the DCA showed that the nomogram had a positive net benefit. Conclusions: A prediction nomogram was developed and validated to assist clinicians in assessing the probability of urosepsis after PCNL.