Impact of patrilocality on contrasting patterns of paternal and maternal heritage in Central-West Africa.

Despite their ancient past and high diversity, African populations are the least represented in human population genetic studies. In this study, uniparental markers (mtDNA and Y chromosome) were used to investigate the impact of sociocultural factors on the genetic diversity and inter-ethnolinguistic gene flow in the three major Nigerian groups: Hausa (n = 89), Yoruba (n = 135) and Igbo (n = 134). The results show a distinct history from the maternal and paternal perspectives. The three Nigerian groups present a similar substrate for mtDNA, but not for the Y chromosome. The two Niger-Congo groups, Yoruba and Igbo, are paternally genetically correlated with populations from the same ethnolinguistic affiliation. Meanwhile, the Hausa is paternally closer to other Afro-Asiatic populations and presented a high diversity of lineages from across Africa. When expanding the analyses to other African populations, it is observed that language did not act as a major barrier to female-mediated gene flow and that the differentiation of paternal lineages is better correlated with linguistic than geographic distances. The results obtained demonstrate the impact of patrilocality, a common and well-established practice in populations from Central-West Africa, in the preservation of the patrilineage gene pool and in the affirmation of identity between groups.

Differentiation between Normal Cognition and Subjective Cognitive Decline in Older Adults Using Discrepancy Scores Derived from Neuropsychological Tests.

Several studies have reported subtle differences in cognition between individuals with subjective cognitive decline (SCD) compared to those with normal cognition. This study aimed to (i) identify these differences using discrepancy scores (e.g., categorial-phonemic verbal fluency performance) derived from neuropsychological tests in three cognitive domains (memory: Wechsler's Word List and Digits; executive functions: Stroop and verbal fluency; and language: BNT and ECCO_Senior) and (ii) determine which discrepancy scores are significant for classification. Seventy-five older adults were included: 32 who were labeled SCD+ (age 71.50 ± 5.29), meeting Jessen et al.'s criteria, and 43 in the normal cognition group (SCD-; age 69.81 ± 4.62). Both groups completed a protocol including screening and the specified neuropsychological tests. No differences were found between the groups in their age, education, episodic memory, global cognitive state, or mood. Significant differences between the groups were observed regarding the discrepancy scores derived from BNT (naming) and ECCO_Senior (sentence comprehension). These scores accurately classified participants (71.6%), with ECCO_Senior having a primary role. ROC curves indicated a poor-to-fair model quality or diagnostic accuracy (AUC_BNT = 0.690; AUC_ECCO = 0.722). In conclusion, discrepancy scores in the language domain are important for distinguishing between individuals with SCD and normal cognition, complementing previous findings in this domain. However, given their relatively poor diagnostic accuracy, they should be used with caution as part of a more detailed neuro-psychological assessment.

Evaluation of the responsiveness pattern to caffeine through a smart data-driven ECG non-linear multi-band analysis.

This study aimed to explore more efficient ways of administering caffeine to the body by investigating the impact of caffeine on the modulation of the nervous system's activity through the analysis of electrocardiographic signals (ECG). An ECG non-linear multi-band analysis using Discrete Wavelet Transform (DWT) was employed to extract various features from healthy individuals exposed to different caffeine consumption methods: expresso coffee (EC), decaffeinated coffee (ED), Caffeine Oral Films (OF_caffeine), and placebo OF (OF_placebo). Non-linear feature distributions representing every ECG minute time series have been selected by PCA with different variance percentages to serve as inputs for 23 machine learning models in a leave-one-out cross-validation process for analyzing the behavior differences between ED/EC and OF_placebo/OF_caffeine groups, respectively, over time. The study generated 50-point accuracy curves per model, representing the discrimination power between groups throughout the 50 min. The best model accuracies for ED/EC varied between 30 and 70 %, (using the decision tree classifier) and OF_placebo/OF_caffeine ranged from 62 to 84 % (using Fine Gaussian). Notably, caffeine delivery through OFs demonstrated effective capacity compared to its placebo counterpart, as evidenced by significant differences in accuracy curves between OF_placebo/OF_caffeine. Caffeine delivery via OFs also exhibited rapid dissolution efficiency and controlled release rate over time, distinguishing it from EC. The study supports the potential of caffeine delivery through Caffeine OFs as a superior technology compared to traditional methods by means of ECG analysis. It highlights the efficiency of OFs in controlling the release of caffeine and underscores their promise for future caffeine delivery systems.

Diagnostic Test Accuracy of Urinary DNA Methylation-based Biomarkers for the Detection of Primary and Recurrent Bladder Cancer: A Systematic Review and Meta-analysis.

BACKGROUND AND OBJECTIVE: Diagnosis of primary and relapsed bladder carcinomas is accomplished by urethrocystoscopy, an invasive procedure, combined with urinary cytology, with limited sensitivity, resulting in a substantial burden. Thus, noninvasive biomarkers have been investigated, among which DNA methylation has shown promise. This systematic review and meta-analysis sought to assess the diagnostic accuracy of DNA methylation biomarkers reported in the literature for bladder cancer detection, pinpointing the most informative one. METHODS: The search for this systematic review and meta-analysis was conducted on PubMed, Scopus, and Cochrane Library for relevant studies published until December 31, 2022. A meta-analysis was performed using a random-effect model, to compute the pooled sensitivity and specificity of the markers. PROSPERO's registration ID for the study is CRD42023397703. KEY FINDINGS AND LIMITATIONS: Out of the 2297 studies retrieved, 68 were included in the final analysis, despite considerable heterogeneity. These involved 12 696 participants, of whom 5557 were diagnosed with bladder cancer. Using diagnostic odds ratio (DOR) as a comparative measure, the five most promising markers (pooled sensitivity, specificity, and DOR) were SALL3 (61%, 97%, and 55.67, respectively), PENK (77%, 93%, and 47.90, respectively), ZNF154 (87%, 90%, and 45.07, respectively), VIM (82%, 90%, and 44.81, respectively), and POU4F2 (81%, 89%, and 34.89, respectively). Urinary cytology identified bladder cancer with 55% sensitivity, 92% specificity, and 14.37 DOR. CONCLUSIONS AND CLINICAL IMPLICATIONS: DNA methylation biomarkers disclose high accuracy for bladder cancer detection in urine. Nonetheless, validation studies in different clinical settings are scarce, hampering clinical use. The identified biomarkers should be prioritized in future validation studies. PATIENT SUMMARY: In this meta-analysis, we include previously published studies that used urine samples of bladder cancer patients' from all around the globe. We were able to compare the diagnostic accuracy of noninvasive markers across different populations. We were able to conclude on the most promising DNA methylation markers to detect bladder cancer using urine.

Comparison of aspiration catheter performance using adaptive pulsatile aspiration in an in vitro thrombectomy model.

OBJECTIVE: Aspiration with a pump or syringe is a mainstay of mechanical thrombectomy (MT) for acute ischemic stroke (AIS), but this technology has seen minimal evolution. Non-continuous adaptive pulsatile aspiration (APA) has been proposed as a potential alternative to standard continuous aspiration as a means of improving revascularization efficiency. METHODS: Using a pathophysiological flow bench model with a synthetic clot, we performed in vitro thrombectomies using the ALGO® Von Vascular, Inc. (Sunrise, FL) APA pump. A total of 25 FDA-approved aspiration catheters were tested, representing inner diameters (ID) from 0.035 in. to 0.088 in. The pump was used in 30 trials with each catheter to remove a simulated M1 occlusion. Revascularization, clot ingestion, time to clot removal, and distal embolization were measured. RESULTS: Among catheters tested using APA, first-pass TICI 3 revascularization was achieved in 100% of the 750 thrombectomy trials using 25 different catheters. There were no distal emboli detected in any trial run. Complete clot ingestion into the pump collection chamber was achieved in 87% to 100% of trials (overall 95%) with clot in the remaining trials corking within the catheter and removed from the model. Time from clot contact to clot removal ranged from 11 s to 90 s (mean 22.6 s, SD 16.8 s), which was negatively correlated with catheter ID (p = 0.007). CONCLUSION: APA via the Von Vascular, Inc. ALGO® pump achieved a high success rate in an in vitro MT model. All catheters tested with the pump achieved complete reperfusion in all trials, and complete clot ingestion into the pump was seen in a majority of trials. The promising in vitro performance of APA using multiple catheters warrants future in vivo investigation.

CDK4/6 inhibitors and endocrine therapy in the treatment of metastatic breast cancer: A real-world and propensity score-adjusted comparison.

INTRODUCTION/BACKGROUND: Hormone Receptor-positive (HR+) and Human Epidermal Growth Factor Receptor 2-negative (HER2-) breast cancer is the most common subtype, predominantly treated with endocrine therapy. The efficacy of CDK4/6 inhibitors combined with endocrine therapy in this context remains to be fully evaluated. MATERIALS (OR PATIENTS) AND METHODS: This study compared the effectiveness of CDK4/6 inhibitors (palbociclib and ribociclib) in combination with an aromatase inhibitor or fulvestrant against endocrine therapy alone in patients with HR+/HER2- advanced breast cancer. The main focus was on progression-free survival (PFS) and overall survival (OS). The study involved a population treated exclusively with endocrine therapy for bone involvement, examining median OS and PFS, and adjusting for variables like stage, visceral metastasis, age, and treatment line. RESULTS: The study found no significant OS difference between treatments with palbociclib, ribociclib, and endocrine therapy alone. However, ribociclib combined with letrozole significantly improved PFS over letrozole alone. Propensity score weighting indicated a potential 50 % reduction in death risk with ribociclib compared to palbociclib, though this was not confirmed by cox regression. CONCLUSION: CDK4/6 inhibitors, particularly ribociclib in combination with letrozole, show promise in improving outcomes for HR+/HER2- breast cancer patients. While palbociclib may not be superior to traditional endocrine therapy, the results underscore the need for further research. These findings could influence future treatment protocols, emphasizing the importance of personalized therapy in this patient group.

Evaluating distributed-learning on real-world obstetrics data: comparing distributed, centralized and local models.

This study focused on comparing distributed learning models with centralized and local models, assessing their efficacy in predicting specific delivery and patient-related outcomes in obstetrics using real-world data. The predictions focus on key moments in the obstetric care process, including discharge and various stages of hospitalization. Our analysis: using 6 different machine learning methods like Decision Trees, Bayesian methods, Stochastic Gradient Descent, K-nearest neighbors, AdaBoost, and Multi-layer Perceptron and 19 different variables with various distributions and types, revealed that distributed models were at least equal, and often superior, to centralized versions and local versions. We also describe thoroughly the preprocessing stage in order to help others implement this method in real-world scenarios. The preprocessing steps included cleaning and harmonizing missing values, handling missing data and encoding categorical variables with multisite logic. Even though the type of machine learning model and the distribution of the outcome variable can impact the result, we reached results of 66% being superior to the centralized and local counterpart and 77% being better than the centralized with AdaBoost. Our experiments also shed light in the preprocessing steps required to implement distributed models in a real-world scenario. Our results advocate for distributed learning as a promising tool for applying machine learning in clinical settings, particularly when privacy and data security are paramount, thus offering a robust solution for privacy-concerned clinical applications.

Classification of Sleep Quality and Aging as a Function of Brain Complexity: A Multiband Non-Linear EEG Analysis.

Understanding and classifying brain states as a function of sleep quality and age has important implications for developing lifestyle-based interventions involving sleep hygiene. Current studies use an algorithm that captures non-linear features of brain complexity to differentiate awake electroencephalography (EEG) states, as a function of age and sleep quality. Fifty-eight participants were assessed using the Pittsburgh Sleep Quality Inventory (PSQI) and awake resting state EEG. Groups were formed based on age and sleep quality (younger adults n = 24, mean age = 24.7 years, SD = 3.43, good sleepers n = 11; older adults n = 34, mean age = 72.87; SD = 4.18, good sleepers n = 9). Ten non-linear features were extracted from multiband EEG analysis to feed several classifiers followed by a leave-one-out cross-validation. Brain state complexity accurately predicted (i) age in good sleepers, with 75% mean accuracy (across all channels) for lower frequencies (alpha, theta, and delta) and 95% accuracy at specific channels (temporal, parietal); and (ii) sleep quality in older groups with moderate accuracy (70 and 72%) across sub-bands with some regions showing greater differences. It also differentiated younger good sleepers from older poor sleepers with 85% mean accuracy across all sub-bands, and 92% at specific channels. Lower accuracy levels (<50%) were achieved in predicting sleep quality in younger adults. The algorithm discriminated older vs. younger groups excellently and could be used to explore intragroup differences in older adults to predict sleep intervention efficiency depending on their brain complexity.

Understanding the mechanism of action of protease inhibitors in controlling the growth of the Candida Genus: potential candidates for development of new antifungal molecules.

There is a growing imperative for research into alternative compounds for the treatment of the fungal infections. Thus, many studies have focused on the analysis of antifungal proteins and peptides from different plant sources. Among these molecules are protease inhibitors (PIs). Previously, PIs present in the peptide-rich fractions called PEF1, PEF2 and PEF3 were identified from Capsicum chinense seeds, which have strong activity against phytopathogenic fungi. The aim of this study was to evaluate the mechanism of action and antimicrobial activity of PIs from PEF2 and PEF3 on the growth of yeasts of the genus Candida. In this work, analyses of their antimicrobial activity and cell viability were carried out. Subsequently, the mechanism of action by which the PIs cause the death of the yeasts was evaluated. Cytotoxicity was assessed in vitro by erythrocytes lysis and in vivo in Galleria mellonella larvae. PEF2 and PEF3 caused 100% of the growth inhibition of C. tropicalis and C. buinensis. For C. albicans inhibition was approximately 60% for both fractions. The PEF2 and PEF3 caused a reduction in mitochondrial functionality of 54% and 46% for C. albicans, 26% and 30% for C. tropicalis, and 71% and 68% for C. buinensis, respectively. These fractions induced morphological alterations, led to membrane permeabilization, elevated ROS levels, and resulted in necrotic cell death in C. tropicalis, whilst demonstrating low toxicity toward host cells. From the results obtained here, we intend to contribute to the understanding of the action of PIs in the control of fungal diseases of medical importance.

Shelf-Life Management and Ripening Assessment of 'Hass' Avocado (Persea americana) Using Deep Learning Approaches.

Avocado production is mostly confined to tropical and subtropical regions, leading to lengthy distribution channels that, coupled with their unpredictable post-harvest behavior, render avocados susceptible to significant loss and waste. To enhance the monitoring of 'Hass' avocado ripening, a data-driven tool was developed using a deep learning approach. This study involved monitoring 478 avocados stored in three distinct storage environments, using a 5-stage Ripening Index to classify each fruit's ripening phase based on their shared characteristics. These categories were paired with daily photographic records of the avocados, resulting in a database of labeled images. Two convolutional neural network models, AlexNet and ResNet-18, were trained using transfer learning techniques to identify distinct ripening indicators, enabling the prediction of ripening stages and shelf-life estimations for new unseen data. The approach achieved a final prediction accuracy of 88.8% for the ripening assessment, with 96.7% of predictions deviating by no more than half a stage from their actual classifications when considering the best side of the samples. The average shelf-life estimates based on the attributed classifications were within 0.92 days of the actual shelf-life, whereas the predictions made by the models had an average deviation of 0.96 days from the actual shelf-life.

Fast Healthcare Interoperability Resources-Based Support System for Predicting Delivery Type: Model Development and Evaluation Study.

BACKGROUND: The escalating prevalence of cesarean delivery globally poses significant health impacts on mothers and newborns. Despite this trend, the underlying reasons for increased cesarean delivery rates, which have risen to 36.3% in Portugal as of 2020, remain unclear. This study delves into these issues within the Portuguese health care context, where national efforts are underway to reduce cesarean delivery occurrences. OBJECTIVE: This paper aims to introduce a machine learning, algorithm-based support system designed to assist clinical teams in identifying potentially unnecessary cesarean deliveries. Key objectives include developing clinical decision support systems for cesarean deliveries using interoperability standards, identifying predictive factors influencing delivery type, assessing the economic impact of implementing this tool, and comparing system outputs with clinicians' decisions. METHODS: This study used retrospective data collected from 9 public Portuguese hospitals, encompassing maternal and fetal data and delivery methods from 2019 to 2020. We used various machine learning algorithms for model development, with light gradient-boosting machine (LightGBM) selected for deployment due to its efficiency. The model's performance was compared with clinician assessments through questionnaires. Additionally, an economic simulation was conducted to evaluate the financial impact on Portuguese public hospitals. RESULTS: The deployed model, based on LightGBM, achieved an area under the receiver operating characteristic curve of 88%. In the trial deployment phase at a single hospital, 3.8% (123/3231) of cases triggered alarms for potentially unnecessary cesarean deliveries. Financial simulation results indicated potential benefits for 30% (15/48) of Portuguese public hospitals with the implementation of our tool. However, this study acknowledges biases in the model, such as combining different vaginal delivery types and focusing on potentially unwarranted cesarean deliveries. CONCLUSIONS: This study presents a promising system capable of identifying potentially incorrect cesarean delivery decisions, with potentially positive implications for medical practice and health care economics. However, it also highlights the challenges and considerations necessary for real-world application, including further evaluation of clinical decision-making impacts and understanding the diverse reasons behind delivery type choices. This study underscores the need for careful implementation and further robust analysis to realize the full potential and real-world applicability of such clinical support systems.

Transcriptomic changes behind Sparus aurata hepatic response to different aquaculture challenges: An RNA-seq study and multiomics integration.

Gilthead seabream (Sparus aurata) is an important species in Mediterranean aquaculture. Rapid intensification of its production and sub-optimal husbandry practices can cause stress, impairing overall fish performance and raising issues related to sustainability, animal welfare, and food safety. The advent of next-generation sequencing technologies has greatly revolutionized the study of fish stress biology, allowing a deeper understanding of the molecular stress responses. Here, we characterized for the first time, using RNA-seq, the different hepatic transcriptome responses of gilthead seabream to common aquaculture challenges, namely overcrowding, net handling, and hypoxia, further integrating them with the liver proteome and metabolome responses. After reference-guided transcriptome assembly, annotation, and differential gene expression analysis, 7, 343, and 654 genes were differentially expressed (adjusted p-value < 0.01, log2|fold-change| >1) in the fish from the overcrowding, net handling, and hypoxia challenged groups, respectively. Gene set enrichment analysis (FDR < 0.05) suggested a scenario of challenge-specific responses, that is, net handling induced ribosomal assembly stress, whereas hypoxia induced DNA replication stress in gilthead seabream hepatocytes, consistent with proteomics and metabolomics' results. However, both responses converged upon the downregulation of insulin growth factor signalling and induction of endoplasmic reticulum stress. These results demonstrate the high phenotypic plasticity of this species and its differential responses to distinct challenging environments at the transcriptomic level. Furthermore, it provides significant resources for characterizing and identifying potentially novel genes that are important for gilthead seabream resilience and aquaculture production efficiency with regard to fish welfare.

Efficacy and safety of repetitive Transcranial Magnetic Stimulation and transcranial Direct Current Stimulation in memory deficits in patients with Alzheimer's disease: Meta-analysis and systematic review.

Repetitive transcranial magnetic stimulation (rTMS) and transcranial direct current stimulation (tDCS) are two of the most used non-pharmacological interventions for Alzheimer's Disease (AD). However, most of the clinical trials have focused on evaluating the effects on global cognition and not on specific cognitive functions. Therefore, considering that memory loss is one of the hallmark symptoms of AD, we aim to assess the efficacy and safety of tDCS and rTMS in memory deficits. For that, multilevel random effect models were performed considering the standardized mean difference (SMD) between active and sham stimulation. A total of 19 studies with 411 participants demonstrated positive effects in memory after tDCS (SMD=0.20, p = 0.04) and rTMS (SMD=0.44, p = 0.001). Subgroup analysis revealed that tDCS had greater efficacy when administered in temporal regions (SMD=0.32, p = 0.04), whereas rTMS was superior when applied in frontal regions (SMD=0.61, p < 0.001). Therefore, depending on the brain region of stimulation, both interventions produced a positive effect on memory symptoms in AD patients. Finally, the safety of both techniques was observed in the AD population after the reporting of almost no serious events.

Methods and considerations for estimating parameters in biophysically detailed neural models with simulation based inference.

Biophysically detailed neural models are a powerful technique to study neural dynamics in health and disease with a growing number of established and openly available models. A major challenge in the use of such models is that parameter inference is an inherently difficult and unsolved problem. Identifying unique parameter distributions that can account for observed neural dynamics, and differences across experimental conditions, is essential to their meaningful use. Recently, simulation based inference (SBI) has been proposed as an approach to perform Bayesian inference to estimate parameters in detailed neural models. SBI overcomes the challenge of not having access to a likelihood function, which has severely limited inference methods in such models, by leveraging advances in deep learning to perform density estimation. While the substantial methodological advancements offered by SBI are promising, their use in large scale biophysically detailed models is challenging and methods for doing so have not been established, particularly when inferring parameters that can account for time series waveforms. We provide guidelines and considerations on how SBI can be applied to estimate time series waveforms in biophysically detailed neural models starting with a simplified example and extending to specific applications to common MEG/EEG waveforms using the the large scale neural modeling framework of the Human Neocortical Neurosolver. Specifically, we describe how to estimate and compare results from example oscillatory and event related potential simulations. We also describe how diagnostics can be used to assess the quality and uniqueness of the posterior estimates. The methods described provide a principled foundation to guide future applications of SBI in a wide variety of applications that use detailed models to study neural dynamics.

MCPIP1 Inhibits Hepatic Stellate Cell Activation in Autocrine and Paracrine Manners, Preventing Liver Fibrosis.

BACKGROUND & AIMS: Hepatic fibrosis is characterized by enhanced deposition of extracellular matrix (ECM), which results from the wound healing response to chronic, repeated injury of any etiology. Upon injury, hepatic stellate cells (HSCs) activate and secrete ECM proteins, forming scar tissue, which leads to liver dysfunction. Monocyte-chemoattractant protein-induced protein 1 (MCPIP1) possesses anti-inflammatory activity, and its overexpression reduces liver injury in septic mice. In addition, mice with liver-specific deletion of Zc3h12a develop features of primary biliary cholangitis. In this study, we investigated the role of MCPIP1 in liver fibrosis and HSC activation. METHODS: We analyzed MCPIP1 levels in patients' fibrotic livers and hepatic cells isolated from fibrotic murine livers. In vitro experiments were conducted on primary HSCs, cholangiocytes, hepatocytes, and LX-2 cells with MCPIP1 overexpression or silencing. RESULTS: MCPIP1 levels are induced in patients' fibrotic livers compared with their nonfibrotic counterparts. Murine models of fibrosis revealed that its level is increased in HSCs and hepatocytes. Moreover, hepatocytes with Mcpip1 deletion trigger HSC activation via the release of connective tissue growth factor. Overexpression of MCPIP1 in LX-2 cells inhibits their activation through the regulation of TGFB1 expression, and this phenotype is reversed upon MCPIP1 silencing. CONCLUSIONS: We demonstrated that MCPIP1 is induced in human fibrotic livers and regulates the activation of HSCs in both autocrine and paracrine manners. Our results indicate that MCPIP1 could have a potential role in the development of liver fibrosis.

Performance of a 74-Microhaplotype Assay in Kinship Analyses.

Microhaplotypes (MHs) consisting of multiple SNPs and indels on short stretches of DNA are new and interesting loci for forensic genetic investigations. In this study, we analysed 74 previously defined MHs in two of the populations that our laboratory provides with forensic genetic services, Danes and Greenlanders. In addition to the 229 SNPs that originally made up the 74 MHs, 66 SNPs and 3 indels were identified in the two populations, and 45 of these variants were included in new definitions of the MHs, whereas 24 SNPs were considered rare and of little value for case work. The average effective number of alleles (Ae) was 3.2, 3.0, and 2.6 in Danes, West Greenlanders, and East Greenlanders, respectively. High levels of linkage disequilibrium were observed in East Greenlanders, which reflects the characteristics of this population that has a small size, and signs of admixture and substructure. Pairwise kinship simulations of full siblings, half-siblings, first cousins, and unrelated individuals were performed using allele frequencies from MHs, STRs and SNPs from Danish and Greenlandic populations. The MH panel outperformed the currently used STR and SNP marker sets and was able to differentiate siblings from unrelated individuals with a 0% false positive rate and a 1.1% false negative rate using an LR threshold of 10,000 in the Danish population. However, the panel was not able to differentiate half-siblings or first cousins from unrelated individuals. The results generated in this study will be used to implement MHs as investigative markers for relationship testing in our laboratory.

Transcriptional signature of host shift in the seed beetle Zabrotes subfasciatus.

In phytophagous insects, adaptation to a new host is a dynamic process, in which early and later steps may be underpinned by different features of the insect genome. Here, we tested the hypothesis that early steps of this process are underpinned by a shift in gene expression patterns. We set up a short-term artificial selection experiment (10 generations) for the use of an alternative host (Cicer arietinum) on populations of the bean beetle Zabrotes subfasciatus. Using Illumina sequencing on young adult females, we show the selected populations differ in the expression of genes associated to stimuli, signalling, and developmental processes. Particularly, the "C. arietinum" population shows upregulation of histone methylation genes, which may constitute a strategy for fine-tuning the insect global gene expression network. Using qPCR on body regions, we demonstrated that the "Phaseolus vulgaris" population upregulates the genes polygalacturonase and egalitarian and that the expression of an odorant receptor transcript variant changes over generations. Moreover, in this population we detected the existence of vitellogenin (Vg) variants in both males and females, possibly harbouring canonical reproductive function in females and extracellular unknown functions in males. This study provides the basis for future genomic investigations seeking to shed light on the nature of the proximate mechanisms involved in promoting differential gene expression associated to insect development and adaptation to new hosts.

Achieving rapid and significant results in healthcare services by using the theory of constraints.

Lack of timeliness and capacity are seen as fundamental problems that jeopardise healthcare delivery systems everywhere. Many believe the shortage of medical providers is causing this timeliness problem. This action research presents how one doctor implemented the theory of constraints (TOC) to improve the throughput (quantity of patients treated) of his ophthalmology imaging practice by 64% in a few weeks with little to no expense. The five focusing steps (5FS) guided the TOC implementation - which included the drum-buffer-rope scheduling and buffer management - and occurred in a matter of days. The implementation provided significant bottom-line results almost immediately. This article explains each step of the 5FS in general terms followed by specific applications to healthcare services, as well as the detailed use in this action research. Although TOC successfully addressed the practice problems, this implementation was not sustained after the TOC champion left the organisation. However, this drawback provided valuable knowledge. The article provides insightful knowledge to help readers implement TOC in their environments to provide immediate and significant results at little to no expense.

Nonlinear Analysis of the Hand and Foot Force-Time Profiles in the Four Competitive Swimming Strokes.

Human locomotion on water depends on the force produced by the swimmer to propel the body forward. Performance of highly complex motor tasks like swimming can yield minor variations that only nonlinear analysis can be sensitive enough to detect. The purpose of the present study was to examine the nonlinear properties of the hand/feet forces and describe their variations across the four competitive swimming strokes performing segmental and full-body swimming. Swimmers performed all-out bouts of 25 m in the four swimming strokes, swimming the full-body stroke, with the arm-pull only and with the leg kicking only. Hand/foot force and swimming velocity were measured. The Higuchi's fractal dimension (HFD) and sample entropy (SampEn) were used for the nonlinear analysis of force and velocity. Both the arm-pull and leg kicking alone were found to produce similar peak and mean hand/foot forces as swimming the full-body stroke. Hand force was more complex in breaststroke and butterfly stroke; conversely, kicking conditions were more complex in front crawl and backstroke. Moreover, the arm-pull and kicking alone tended to be more complex (higher HFD) but more predictable (lower SampEn) than while swimming the full-body stroke. There was no loss of force production from segmental swimming to the full-body counterpart. In conclusion, the number of segments in action influences the nonlinear behavior of the force produced and, when combining the four limbs, the complexity of the hand/foot force tends to decrease.

Subacute Neurotoxicity as an Adverse Reaction to Ceftazidime: A Case Report.

Neurotoxicity associated with cephalosporins is an increasingly recognized complication, although among cephalosporins, ceftazidime is rarely reported for such an adverse reaction. Moreover, subacute, rather than acute, presentation of neurotoxicity associated with cephalosporins is rare. A 77-year-old female patient with stage 4 chronic renal disease was admitted due to cellulitis in her right lower limb, multiorgan dysfunction complicated by oliguric acute kidney injury, and a need for hemodialysis via a central venous catheter. On the 13th day after admission, she became febrile, and bacteremia associated with a central venous catheter was identified, which prompted the initiation of empirical antibiotic therapy with vancomycin and ceftazidime. After 13 days of antibiotic therapy with vancomycin and ceftazidime, the patient became confused, with temporal-spatial disorientation and myoclonus, especially in the upper limbs, with worsening renal function. Ceftazidime was discontinued, and the patient's condition improved with complete remission of symptoms on the 8th day after symptom onset. Neurotoxicity associated with ceftazidime is a rare but probably underdiagnosed adverse reaction. It is more frequent in elderly patients with previous neurological dysfunction and end-stage kidney disease and/or acute kidney injury, and it usually manifests soon after starting treatment. Early identification and monitoring of risk factors and symptoms should lead the physician to a rapid withdrawal of the offending drug.