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Urbanization, socioeconomic status, and exposure to PM2.5, associated with township-based cerebrovascular disease (CBD) mortality

Background/Objective Previous research has shown an association between socioeconomic status (SES) and mortality, particularly in chronic diseases. However, limited studies simultaneously examined the relationship between urbanization, SES, exposure to PM2.5, and cerebrovascular disease (CBD) mortality at a township level from 2011 to 2020 in Taiwan. Methods Township-level SES data (percentages of low-income and education with college and above) and seven levels of urbanization from 2011 to 2020 were obtained from data sources in Taiwan’s central government. Age-standardized CBD mortality rates in 358 townships were calculated using the Geographic Information System (GIS) provided by the Research Center for the Humanities and Social Sciences (RCHSS) at Academia Sinica. Exposure to PM2.5 concentration was estimated using a combination of land-use regression and Ordinary Kriging to enhance the robustness of PM2.5 concentration estimates at the township level. Panel regression and structural equation modeling (SEM) was employed to analyze the association between urbanization, SES, exposure to PM2.5, and township-based CBD mortality rates. Results There are significant differences in SES variables and exposure to PM2.5 among townships with seven levels of urbanization (P < 0.001). Even after controlling for other covariates (SES and PM2.5 concentration) through multivariate analysis, the associations between CBD mortality rates and urbanization areas persisted. SEM analysis revealed a negative correlation between age-standardized CBD mortality rate and education levels (β = −0.22), but a positive correlation with the proportion of low-income individuals (β = 0.41). There was no significant association between exposure to PM2.5 and CBD mortality. The panel regression analysis revealed that socioeconomic variables had different effects on CBD mortality rates across the three models (pooled ordinary least squares, fixed-effects, and random-effects) in both urban and rural areas. Notably, the level of urbanization was observed to modify the relationship between socioeconomic variables and CBD mortality rates. Conclusion Our findings suggest that township-based CBD mortality is significantly associated with SES variables and levels of urbanization, despite a reduction in CBD mortality from 2011 to 2020. Therefore, targeted intervention programs should be implemented to reduce CBD mortality in different levels of urbanization, particularly in remote townships. It is necessary to assess the disparities in socioeconomic status to achieve a fair allocation of resources at the township level.

Time to achieve optimal glycemic control and its determinants among diabetes mellitus patients receiving treatment: a retrospective study

Abstract Diabetes mellitus (DM) is a major public health problem responsible for morbidity and mortality. Maintaining blood sugar control helps patients achieve optimal glycemic levels. Therefore, this study aimed to identify the factors affecting the time to achieve optimal glycemic control among DM patients at Assosa General Hospital (AGH), Western Ethiopia. A retrospective study design was conducted from 427 randomly selected DM patients in the outpatient department (OPD) clinic at AGH under the follow-up period from September 2022 to September 2024. The median survival time, Kaplan-Meier survival estimate, and Log-Rank test were used to describe the data and compare the survival time between groups. The study used Cox PH model to analyze the time to achieve optimal glycemic control of DM patients, where hazard ratio, p-value, and 95% CI for hazard ratio were used for testing significance. Schoenfeld and Cox-Snell residuals were used to check the model assumptions. The median time to optimal glycemic control for DM patients was 12 months. At the end of the follow-up, 74.2% of the patients had developed an event and the rest 25.8% were censored. The significant predictors of time to optimal glycemic control include: older age (AHR = 0.871(95% CI 0.809, 0.937)), females (AHR = 1.295 (95% CI 1.024, 1.639)), having FHDM (AHR = 1.681(95% CI 1.313, 2.153)), rural residence(AHR = 0.463(95% CI 0.354, 0.607)), presence of comorbidity (AHR = 0.508(95% CI 0.302, 0.854)), DM related complications (AHR = 0.419(95% CI 0.326, 0.539)), high BLBGL AHR = 0.997(95% CI 0.995, 0.998)). This study found the factors that prolonged or shortened the time to reach optimal glycaemic control for T2DM patients. The study revealed that older age, male patients, patients having other related comorbidities and patients with no FHDM, patients having DM-related complications as poor prognostic factors of T2DM disease and also prolonged recovery time. Therefore, attention should be given to these patients to obtain good glycaemic levels and the patient being healthy.

Simple, rapid, and efficient purification of M13 phages: The Faj-elek method

M13 bacteriophages (phages) are used as very important tools in molecular biology, biotechnology and, nanotechnology. Many methods have been developed so far for the purification of these phages. However, it is important that phages retain their infecting properties, especially in biotechnological applications such as phage display technology. The most widely used is the PEG precipitation method, but it has some limitations such as impurities and reduced infectivity. To overcome them, we developed a new method for purification of M13 bacteriophages using syringe filters made of cellulose acetate membranes with a pore diameter of 0.22 µm. Phages were aggregated so that they could remain on the filters and for this purpose, the pH of the phage cultures was reduced to 3. The phage cultures were filtered and then the phages were recovered in tris-buffered saline (TBS) buffer (pH 10.5) by reversing the filter. The recovery rate was 250% higher than the standard PEG method. This new Faj-elek method offers an alternative to existing methods, allowing cheaper, easier and faster purification of M13 phages using syringe filters available in every research laboratory.

The combination of the 18F-FDG PET and susceptibility-weighted imaging for diagnosis of cerebral glucose metabolism and iron deposition in Parkinson’s disease

Cocaine self-administration attenuates brain glucose metabolism and functional connectivity in rats

Background Cocaine abuse and Cocaine Use Disorder (CUD) is an increasingly urgent public health issue leading to major health risks often resulting in a decreased lifespan and quality of life. Previous human research has described brain function of cocaine addicts however the amount of cocaine use, duration of use, and exclusion of using other drugs (i.e., nicotine and alcohol) have all been difficult to control. One unanswered question is related to how does cocaine affect both brain glucose metabolism and functional connectivity?. Methods The present study examined using positron emission tomography (PET) imaging and the glucose analog [18F]-Fluorodeoxyglucose (18F-FDG), brain glucose metabolism (BGluM) and functional connectivity in male rats (N = 6) that self-administered cocaine compared to baseline control scans in the same animals prior to cocaine exposure. Results Our Results showed that Cocaine Self-Administration (CSA) caused significant BGluM decreases in several brain regions including posterior thalamic nuclei, Claustrum (Cl); Solitary nucleus, Presubiculum (PrS); Caudate Putamen (CPu); Anterior hypothalamic area (AHA); Ventral pallidum (VP); and amygdala. Activation (increased BGluM) was observed in the primary somatosensory cortex. These regions are associated with memory, spatial navigation, visual processing and saliency along with other somatosensory and motor functions, as well as regulatory autonomic function (cardiovascular) and hormonal response. Conclusion This brain functional connectivity mapping illustrated a brain circuit composed of brain regions that are either a part of or connect with the mesolimbic reward pathway that is mediated by dopamine. When this circuit is dysregulated, it is believed to be associated with substance use disorders and reward dysregulation which have recently been described as attributes of preaddiction.

White matter properties in fronto-parietal tracts predict maladaptive functional activation and deficient response inhibition in ADHD

Abstract Response inhibition is a key characteristic of adaptive human behaviour. However, in attention deficit hyperactivity disorder (ADHD) it is often impaired. Previous neuroimaging investigations implicate a myriad of brain networks in response inhibition, making it more difficult to understand and overcome response inhibition difficulties. Recently, it has been suggested that a specific fronto-parietal functional circuitry between the inferior frontal gyrus (IFG) and the intraparietal sulcus (IPS), dictates the recruitment of the IPS during response inhibition in ADHD. To ascertain the critical role of the IFG-IPS functional circuit and its relevance to response inhibition in ADHD, it is crucial to understand the underlying structural architecture of this circuit so that the functional relevance could be interpreted correctly. Here we investigated the white matter pathways connecting the IFG and IPS using seed-based probabilistic tractography on diffusion data in 33 ADHD and 19 neurotypicals, assessing their impact on both IPS recruitment during response inhibition and on response inhibition performance in a Go/No-go task. Our results showed that individual differences in the structural properties of the IPS-IFG circuit, including tract volume and diffusivity, were linked to IPS activation and even predicted response inhibition performance outside the scanner. These findings highlight the structural-functional coupling within the IFG-IPS circuit in response inhibition in ADHD and suggest a structural basis for maladaptive functional top-down control in deficient inhibition in ADHD.

The effect of follicular and ampullary fluid extracellular vesicles on bovine oocyte competence and in vitro fertilization rates

Follicular fluid from preovulatory follicles as well as ampullary fluid from slaughtered cows at the early metestrus were collected for isolation of EVs. Excellent and good quality bovine oocytes were selected and distributed into four groups: control group which did not receive EVs, the FFEV group which were exposed to 40 μg/ml of follicular fluid EVs for the first 18 hours of the culture, the FFAFEV group which received 40 μg/ml of follicular fluid EVs for the first 18 hours of the culture, followed by 3.4 μg/ml of ampullary fluid EVs for the remaining 4.5 hours, and the AFEV group which were exposed to 3.4 μg/ml of ampullary fluid EVs for the final 4.5 hours of the culture. After a total incubation period of 22.5 hours, the COCs were evaluated for nuclear maturation, expression of some relevant genes, and Raman spectra from different areas of the representative matured oocytes (Experiment 1). In addition, fertilization rate of the oocytes was assessed after addition of EVs to the maturation medium (Experiment 2). The maturation and fertilization rates, as well as the expression of TNFAIP6, HAS2, and GDF9 genes, were significantly higher in the EVs treatment groups compared to the control group (p ≤ 0.05). Furthermore, the Raman microspectroscopy revealed a higher number of mitochondria (1602 cm-1), increased levels of unsaturated lipids (1655 cm-1) and a favorable phenylalanine to carbohydrate ratio (1002/1037, serving as a marker for oocyte quality, in the FFAFEV group compared to the control group. Additionally, there was a lower concentration of saturated lipids (2883 cm-1) in the FFAFEV group than in the control group. In conclusion, our findings showed that supplementation of the oocyte maturation media with follicular and ampullary fluid EVs positively influenced oocyte quality and enhanced in vitro maturation, fertilization rates, and the relevant gene expression.

Optimal positioning of biomarkers according to ulcerative colitis activity

Expressing intrinsically-disordered tardigrade proteins has positive effects on acute but not chronic stress tolerance in Saccharomyces cerevisiae

The production of high value and commodity chemicals, biopharmaceuticals and biofuels using Saccharomyces cerevisiae is hindered by various stress factors that affect yield and efficiency. Tardigrades, known for their remarkable stress tolerance, express unique proteins responsible for their resilience. This study evaluates the impact of expressing the tardigrade proteins CAHS3, MAHS, and RvLEAM on stress tolerance in S. cerevisiae. Our results show that high yields of these proteins do not impede yeast growth, except for CAHS3, which reduces proliferation. Expression of MAHS enhances acute heat tolerance, while MAHS and RvLEAM confer increased tolerance to acute hyperosmotic stress. Both CAHS3 and RvLEAM improve desiccation survival. However, these proteins do not provide benefits under chronic stress conditions such as prolonged exposure to high temperature, hyperosmotic stress, or solvents. These findings highlight the potential utility of tardigrade proteins for transient stress protection in industrial bioprocesses and suggest future engineering approaches for improved stress tolerance in yeast.

Research and simulation analysis of Jack based dental treatment chair human–machine system

Survival time and prognostic factors in dogs clinically diagnosed with haemangiosarcoma in UK first opinion practice

Visceral haemangiosarcoma is considered clinically aggressive in dogs, with perceived poor prognosis often leading to euthanasia at presentation. This study aimed to determine survival times and prognostic factors in dogs with haemangiosarcoma under first-opinion care. Dogs clinically diagnosed with haemangiosarcoma in first-opinion practice in 2019 were identified in VetCompass electronic health records and examined to capture variables potentially associated with survival. Median survival time (MST) from diagnosis was calculated for the whole population, those histopathologically confirmed and based on primary tumour location. Binary logistic regression was used to explore differences between dogs that died on the day of diagnosis and those that survived ≥1 day. Cox proportional hazards modelling explored factors associated with time to death in dogs surviving ≥1 day. Across all cases (n = 788), overall MST was 9.0 days (95%CI:5.0–15.0, range: 0–1789) and proportional 1-year survival was 12.0% (95%CI:9.7–15.0%). Dogs with splenic (MST = 4.0 days, 95%CI 0.0–9.0) and cutaneous haemangiosarcoma (MST = 119.0 days,95%CI:85.0–248.0) had MST greater than 0 days. Of dogs with a histopathological diagnosis of haemangiosarcoma, overall MST was longer at 105 days (95% CI 84–133 days) and additionally, location-specific MST were longer. For both clinically diagnosed cases and histopathologically confirmed cases, increasing tumour size was associated with increased hazard of death while cutaneous location and surgery were associated with reduced hazard of death. A very short survival time was identified for haemangiosarcoma under first-opinion care. Although survival time was longest for cutaneous cases, the actualised prognosis was poor overall for haemangiosarcoma. However, a common prevailing view of extremely poor prognosis for haemangiosarcoma could be promoting frequent euthanasia at presentation and therefore leading to a self-fulfilling prophecy and low survival times. Further exploration of the potential effect of perceived prognosis is warranted. This study provides valuable information for contextualised care and dialogues with clients in first-opinion practice.

SMARCB1 orchestrates cellular plasticity and oncogenic pathways in typical and chondroid chordomas

Physiological and psychological responses to five-day fasting

The objective of this study was to examine the variations in adipokines, myokines, inflammation indicators, glucose, insulin, and ketones in the body over a 5-day fasting period. Additionally, the study aimed to investigate the underlying factors contributing to changes in body mass index (BMI) and fat mass. These factors included blood markers, participants’ healthy lifestyle habits, emotional intelligence, personality traits, impulsivity, overall well-being, and subjective happiness. The study involved 42 women with an average age of 49.8 years (± 9.3 years). The following indicators were measured: leptin, adiponectin, TNF-alpha, BDNF, irisin, IL-6, insulin, glucose levels, and ketone bodies. Various assessments were utilized, including the Physical Activity Questionnaire, the Brunel Mood Scale (BRUMS-LTU), the Schutte Self-Report Emotional Intelligence Test, the 10-item Perceived Stress Scale, and the Big Five personality traits. The results showed that fasting led to substantial reductions in body mass, waist circumference, leptin levels, glucose, and insulin levels, while simultaneously increasing ketone bodies. Basal energy expenditure decreased, but participants experienced improvements in mood, with increased vigor and reduced tension. Although markers of inflammation rose, the concentration of irisin declined, while levels of BDNF and adiponectin remained unchanged. Moreover, a greater reduction in fat mass was associated with higher pre-fasting well-being, emotional intelligence, and lower levels of tension and impulsivity. Conversely, the loss of lean mass was linked to neuroticism and higher levels of impulsivity, particularly concerning pre-fasting tension levels.These findings suggest that psychological factors may impact fasting outcomes, emphasizing the need for personalized fasting strategies.

Classification of spinopelvic balance in ambulatory adolescents and adults with cerebral palsy: a cross-sectional study

A Mallows-like criterion for anomaly detection with random forest implementation

Anomaly detection plays a crucial role in fields such as information security and industrial production. It relies on the identification of rare instances that deviate significantly from expected patterns. Reliance on a single model can introduce uncertainty, as it may not adequately capture the complexity and variability inherent in real-world datasets. Under the framework of model averaging, this paper proposes a criterion for the selection of weights in the aggregation of multiple models, employing a focal loss function with Mallows’ form to assign weights to the base models. This strategy is integrated into a random forest algorithm by replacing the conventional voting method. Empirical evaluations conducted on multiple benchmark datasets demonstrate that the proposed method outperforms classical anomaly detection algorithms while surpassing conventional model averaging techniques based on minimizing standard loss functions. These results highlight a notable enhancement in both accuracy and robustness, indicating that model averaging methods can effectively mitigate the challenges posed by data imbalance.

Enhancing electric vehicle powertrain energy efficiency using robust nonlinear control approaches

Abstract This paper addresses the issue of controlling the drivetrain of electric vehicles. Taking into account both internal and external system disturbances, including the vehicle’s mass, rotational friction of the shafts, wind speed, vehicle aerodynamics, road type, and slope constraints, the controller’s task is to ensure robustness in vehicle behavior. The significant dynamics of these disturbances and uncertainties in vehicle parameters have a substantial impact on vehicle performance. To overcome these challenges, a nonlinear model of the entire controlled system is developed. Subsequently, a robust nonlinear controller is designed using the damping function version of the backstepping design technique to compensate for all uncertain terms. Within this framework, two primary control loops are established. Firstly, a speed control loop is implemented to achieve precise tracking of the driver’s speed reference. Secondly, the machine current is optimized to generate maximum torque. A formal analysis based on Lyapunov stability is conducted to describe the control system’s performance. Despite parameter uncertainties, it is demonstrated that all control objectives are asymptotically achieved. Ultimately, all control objectives are validated through simulation results using Matlab/Simulink, showcasing the efficiency and robustness of the proposed control technique.

Blockchain-based zero trust networks with federated transfer learning for IoT security in industry 5.0

The rise of Industry 5.0 focuses on merging advanced intelligence, automation, and human-centered teamwork in industrial settings. However, keeping interconnected IoT networks secure is still a challenging problem. This paper proposes a new security framework that combines Blockchain, Federated Transfer Learning, and zero trust network (ZTN) principles to improve IoT security in Industry 5.0. Blockchain is a decentralized ledger that ensures secure data sharing and protects model updates. Federated Transfer Learning allows model training across distributed IoT devices to keep data private. The ZTN approach enforces strict access rules, assuming that no entity is trusted by default. The proposed framework offers a scalable and resilient solution to protect next-generation industrial IoT networks, using Blockchain for data security, transfer learning for adaptability, and ZTN for strict access control. The ZTN architecture strengthens security by checking every access request and keeping the IoT system safe. The experimental results show good performance of the proposed method, with better accuracy, precision, recall, and F1 scores. The model achieved an accuracy of 0.85, 0.88, and 0.87 for learning rates of 0.01, 0.001, and 0.0001, respectively, at 100 epochs. The precision values reached 0.84, 0.87, and 0.86, while the recall scores were 0.82, 0.86, and 0.85, respectively. The F1-scores were recorded at 0.83, 0.86, and 0.85, which confirms the robustness of our model.

Characterization of glycoside hydrolases involved in xyloglucan degradation in the thermophilic bacterium Thermotoga maritima

Toward resilient cities: Mapping the interconnected factors shaping urbanization in a dual analysis framework

Utilizing the Push and Pull theory, this study examines the impact of socio-economic disparities and natural disasters on migration and urbanization. With a global significance, the shift of population from rural to urban areas carries profound implications for societies and economies. In the specific context of Pakistan, the research delves into the driving forces behind the rapid urbanization in Karachi and Quetta. Employing a mixed-methods approach, combining quantitative data and qualitative Geographic Information System (GIS) analysis, the study surveyed 1120 migrants. Results indicate a significant positive correlation between socio-economic disparities, natural disasters, and migration, highlighting the interplay of rural push factors and urban pull factors. GIS and satellite images reveal noticeable expansion in covered areas in both cities. The study underscores the importance of effective disaster management and resilient infrastructure to mitigate the impact of natural disasters on migration and urbanization. The findings offer valuable insights for policymakers and academics, discussed in the later sections of the study.

Optimized breast cancer diagnosis using self-adaptive quantum metaheuristic feature selection

Abstract Breast cancer is a leading cause of mortality among women and is increasing rapidly around the world. For early diagnosis of breast cancer, precise classification, and finding the best subset for cancer identification, evolutionary-based feature selection methods play a vital role in effective treatment. Previous studies have shown that existing evolutionary methods are complicated in correctly differentiating BC disease subtypes with high consistency, which seriously affects the performance of classification methods. To prevent diagnostic errors with hostile implications for patient health, in this study, we develop a new evolutionary method called SeQTLBOGA that incorporates the learner quantization before the search capability of the feature space to prevent premature falls into the local optima. In the SeQTLBOGA algorithm, quantum theory and a self-adaptive mechanism are employed to update the Teaching Learning-based Optimization (TLBO) rule to enhance convergence search capabilities. Most importantly, a self-adaptive genetic algorithm (GA) is also incorporated into TLBO to tradeoff between exploration and exploitation to handle slow convergence and exploitation competence, and simultaneously optimizing parameters of support vector machines (SVM) and the best features subset is our primary objective. Comparative results based on optimal computing time and performance are also offered to empirically analyze the traditional algorithms. Therefore, this paper aims to evaluate the most recent quantum-inspired metaheuristic algorithms in WBCD and WDBC databases, emphasizing their advantages and disadvantages.