Dosing time optimization of antihypertensive medications by including the circadian rhythm in pharmacokinetic-pharmacodynamic models.

Blood pressure (BP) follows a circadian variation, increasing during active hours, showing a small postprandial valley and a deeper decrease during sleep. Nighttime reduction of 10-20% relative to daytime BP is defined as a dipper pattern, and a reduction of less than 10%, as a non-dipper pattern. Despite this BP variability, hypertension's diagnostic criteria and therapeutic objectives are usually based on BP average values. Indeed, studies have shown that chrono-pharmacological optimization significantly reduces long-term cardiovascular risk if a BP dipper pattern is maintained. Changes in the effect of antihypertensive medications can be explained by circadian variations in their pharmacokinetics (PK) and pharmacodynamics (PD). Nevertheless, BP circadian variation has been scarcely included in PK-PD models of antihypertensive medications to date. In this work, we developed PK-PD models that include circadian rhythm to find the optimal dosing time (Ta) of first-line antihypertensive medications for dipper and non-dipper patterns. The parameters of the PK-PD models were estimated using global optimization, and models were selected according to the lowest corrected Akaike information criterion value. Simultaneously, sensitivity and identifiability analysis were performed to determine the relevance of the parameters and establish those that can be estimated. Subsequently, Ta parameters were optimized to maximize the effect on BP average, BP peaks, and sleep-time dip. As a result, all selected models included at least one circadian PK component, and circadian parameters had the highest sensitivity. Furthermore, Ta with which BP>130/80 mmHg and a dip of 10-20% are achieved were proposed when possible. We show that the optimal Ta depends on the therapeutic objective, the medication, and the BP profile. Therefore, our results suggest making chrono-pharmacological recommendations in a personalized way.

Alternative language paradigms for functional magnetic resonance imaging as presurgical tools for inducing crossed cerebro-cerebellar language activations in brain tumor patients.

OBJECTIVES: Crossed cerebro-cerebellar BOLD activations have recently come to light as additional diagnostic features for patients with brain tumors. The covert verb generation (VG) task is a widely used language paradigm to determine these language-related crossed activations. Here we demonstrate these crossed activations in two additional language paradigms, the semantic and phonological association tasks. We propose the merit of these tasks to language lateralization determination in the clinic as they are easy to monitor and suitable for patients with aphasia. METHODS: Patients with brain tumors localized at different cortical sites (n = 71) performed three language paradigms, namely the VG task as well as the semantic (SA) and phonological (PA) association tasks with button-press responses. Respective language activations in disparate cortical regions and the cerebellum were assigned laterality. Agreements in laterality between the two new tasks and the verb generation task were tested using Cohen's kappa. RESULTS: Both tasks significantly agreed in cortical and cerebellar lateralization with the verb generation task in patients. Additionally, a McNemar test confirmed the presence of crossed activations in the cortex and the cerebellum in the entire subject population. CONCLUSION: We demonstrated that the semantic and phonological association tasks resulted in crossed cerebro-cerebellar language lateralization activations as those observed due to the covert verb generation task. This may suggest the possibility of these tasks being used conjointly with the traditional verb generation task, especially for subjects that may be unable to perform the latter. KEY POINTS: • The semantic and phonological association tasks can be useful as additional presurgical fMRI language lateralization paradigms for brain tumor patients along with the standard verb generation task. • All three tasks also confirm the presence of crossed cerebro-cerebellar language activations in the current subject population.

A case-control study of a combination of single nucleotide polymorphisms and clinical parameters to predict clinically relevant toxicity associated with fluoropyrimidine and platinum-based chemotherapy in gastric cancer.

BACKGROUND: Fluoropyrimidine plus platinum chemotherapy remains the standard first line treatment for gastric cancer (GC). Guidelines exist for the clinical interpretation of four DPYD genotypes related to severe fluoropyrimidine toxicity within European populations. However, the frequency of these single nucleotide polymorphisms (SNPs) in the Latin American population is low (< 0.7%). No guidelines have been development for platinum. Herein, we present association between clinical factors and common SNPs in the development of grade 3-4 toxicity. METHODS: Retrospectively, 224 clinical records of GC patient were screened, of which 93 patients were incorporated into the study. Eleven SNPs with minor allelic frequency above 5% in GSTP1, ERCC2, ERCC1, TP53, UMPS, SHMT1, MTHFR, ABCC2 and DPYD were assessed. Association between patient clinical characteristics and toxicity was estimated using logistic regression models and classification algorithms. RESULTS: Reported grade ≤ 2 and 3-4 toxicities were 64.6% (61/93) and 34.4% (32/93) respectively. Selected DPYD SNPs were associated with higher toxicity (rs1801265; OR = 4.20; 95% CI = 1.70-10.95, p = 0.002), while others displayed a trend towards lower toxicity (rs1801159; OR = 0.45; 95% CI = 0.19-1.08; p = 0.071). Combination of paired SNPs demonstrated significant associations in DPYD (rs1801265), UMPS (rs1801019), ABCC2 (rs717620) and SHMT1 (rs1979277). Using multivariate logistic regression that combined age, sex, peri-operative chemotherapy, 5-FU regimen, the binary combination of the SNPs DPYD (rs1801265) + ABCC2 (rs717620), and DPYD (rs1801159) displayed the best predictive performance. A nomogram was constructed to assess the risk of developing overall toxicity. CONCLUSION: Pending further validation, this model could predict chemotherapy associated toxicity and improve GC patient quality of life.

Protein quantification by bicinchoninic acid (BCA) assay follows complex kinetics and can be performed at short incubation times.

Amongst the available methodologies for protein determination, the bicinchoninic acid (BCA) assay highlights for its simplicity, sensitivity, repeatability and reproducibility. Nevertheless, in spite that the general principle behind this methodology is known, there are still unanswered questions regarding the chemistry behind the assay and the experimental conditions commonly employed. The present work explored the kinetics, and the analytical response of the assay to free amino acids, peptides (containing tryptophan and tyrosine), and proteins. Results revealed kinetic profiles characterized by the absence of plateaus, with behaviors depending on the type of the sample. The latter, along with contribution to the BCA index elicited by oxidation products generated at the side chain of tryptophan and tyrosine, as well as pre-oxidized ß-casein, evidenced the presence of complex reaction mechanisms. In spite of such complexity, our results showed that the BCA index is not modulated by the incubation time. This applies for responses producing absorbance intensities (at 562 nm) higher than 0.1. Therefore, we propose that the assay can be applied at shorter incubation times (15 min) than those indicated in manufactures specifications, and usually used by researches and industry (30 min at 37 °C).

Using metabolic networks to predict cross-feeding and competition interactions between microorganisms.

Understanding the interactions between microorganisms and their impact on bacterial behavior at the community level is a key research topic in microbiology. Different methods, relying on experimental or mathematical approaches based on the diverse properties of bacteria, are currently employed to study these interactions. Recently, the use of metabolic networks to understand the interactions between bacterial pairs has increased, highlighting the relevance of this approach in characterizing bacteria. In this study, we leverage the representation of bacteria through their metabolic networks to build a predictive model aimed at reducing the number of experimental assays required for designing bacterial consortia with specific behaviors. Our novel method for predicting cross-feeding or competition interactions between pairs of microorganisms utilizes metabolic network features. Machine learning classifiers are employed to determine the type of interaction from automatically reconstructed metabolic networks. Several algorithms were assessed and selected based on comprehensive testing and careful separation of manually compiled data sets obtained from literature sources. We used different classification algorithms, including K Nearest Neighbors, XGBoost, Support Vector Machine, and Random Forest, tested different parameter values, and implemented several data curation approaches to reduce the biological bias associated with our data set, ultimately achieving an accuracy of over 0.9. Our method holds substantial potential to advance the understanding of community behavior and contribute to the development of more effective approaches for consortia design.IMPORTANCEUnderstanding bacterial interactions at the community level is critical for microbiology, and leveraging metabolic networks presents an efficient and effective approach. The introduction of this novel method for predicting interactions through machine learning classifiers has the potential to advance the field by reducing the number of experimental assays required and contributing to the development of more effective bacterial consortia.

Competitive oxidation of key pentose phosphate pathway enzymes modulates the fate of intermediates and NAPDH production.

The oxidative phase of the pentose phosphate pathway (PPP) involving the enzymes glucose-6-phosphate dehydrogenase (G6PDH), 6-phosphogluconolactonase (6PGL), and 6-phosphogluconate dehydrogenase (6PGDH), is critical to NADPH generation within cells, with these enzymes catalyzing the conversion of glucose-6-phosphate (G6P) into ribulose-5-phosphate (Ribu5-P). We have previously studied peroxyl radical (ROO•) mediated oxidative inactivation of E. coli G6PDH, 6PGL, and 6PGDH. However, these data were obtained from experiments where each enzyme was independently exposed to ROO•, a condition not reflecting biological reality. In this work we investigated how NADPH production is modulated when these enzymes are jointly exposed to ROO•. Enzyme mixtures (1:1:1 ratio) were exposed to ROO• produced from thermolysis of 100 mM 2,2'-azobis(2-methylpropionamidine) dihydrochloride (AAPH). NADPH was quantified at 340 nm, and protein oxidation analyzed by liquid chromatography with mass spectrometric detection (LC-MS). The data obtained were rationalized using a mathematical model. The mixture of non-oxidized enzymes, G6P and NADP+ generated ∼175 µM NADPH. Computational simulations showed a constant decrease of G6P associated with NADPH formation, consistent with experimental data. When the enzyme mixture was exposed to AAPH (3 h, 37 ºC), lower levels of NADPH were detected (∼100 µM) which also fitted with computational simulations. LC-MS analyses indicated modifications at Tyr, Trp, and Met residues but at lower concentrations than detected for the isolated enzymes. Quantification of NADPH generation showed that the pathway activity was not altered during the initial stages of the oxidations, consistent with a buffering role of G6PDH towards inactivation of the oxidative phase of the pathway.

Deep Learning-Based Glucose Prediction Models: A Guide for Practitioners and a Curated Dataset for Improved Diabetes Management.

Accurate short- and mid-term blood glucose predictions are crucial for patients with diabetes struggling to maintain healthy glucose levels, as well as for individuals at risk of developing the disease. Consequently, numerous efforts from the scientific community have focused on developing predictive models for glucose levels. This study harnesses physiological data collected from wearable sensors to construct a series of data-driven models based on deep learning approaches. We systematically compare these models to offer insights for practitioners and researchers venturing into glucose prediction using deep learning techniques. Key questions addressed in this work encompass the comparison of various deep learning architectures for this task, determining the optimal set of input variables for accurate glucose prediction, comparing population-wide, fine-tuned, and personalized models, and assessing the impact of an individual's data volume on model performance. Additionally, as part of our outcomes, we introduce a meticulously curated dataset inclusive of data from both healthy individuals and those with diabetes, recorded in free-living conditions. This dataset aims to foster research in this domain and facilitate equitable comparisons among researchers.

Baroreflex and chemoreflex interaction in high-altitude exposure: possible role on exercise performance.

The hypoxic chemoreflex and the arterial baroreflex are implicated in the ventilatory response to exercise. It is well known that long-term exercise training increases parasympathetic and decreases sympathetic tone, both processes influenced by the arterial baroreflex and hypoxic chemoreflex function. Hypobaric hypoxia (i.e., high altitude [HA]) markedly reduces exercise capacity associated with autonomic reflexes. Indeed, a reduced exercise capacity has been found, paralleled by a baroreflex-related parasympathetic withdrawal and a pronounced chemoreflex potentiation. Additionally, it is well known that the baroreflex and chemoreflex interact, and during activation by hypoxia, the chemoreflex is predominant over the baroreflex. Thus, the baroreflex function impairment may likely facilitate the exercise deterioration through the reduction of parasympathetic tone following acute HA exposure, secondary to the chemoreflex activation. Therefore, the main goal of this review is to describe the main physiological mechanisms controlling baro- and chemoreflex function and their role in exercise capacity during HA exposure.

Stability of Multicomponent Antidote Parenteral Formulations for Autoinjectors against Chemical War Agents (Neurotoxics).

Combinations of different drugs are formulated in autoinjectors for parenteral administration against neurotoxic war agents. In this work, the effects on the chemical stability of the following three variables were studied: (i) type of drug combination (pralidoxime, atropine, and midazolam versus obidoxime, atropine, and midazolam); (ii) pH (3 versus 4); and (iii) type of elastomeric sealing material (PH 701/50 C BLACK versus 4023/50 GRAY). Syringes were stored at three different temperatures: 4, 25, and 40 °C. Samples were assayed at different time points to study the physical appearance, drug sorption on the sealing elastomeric materials, and drug content in solution. Midazolam was unstable in all tested experimental conditions. Drug adsorption was observed in both types of sealing elastomeric materials and was significantly (p < 0.01) dependent on the lipophilicity of the drug. The most stable formulation was the combination of pralidoxime and atropine at pH 4 with the elastomeric sealing material 4023/50 GRAY.

Attenuated thermoregulatory, metabolic, and liver acute phase protein response to heat stroke in TNF receptor knockout mice.

Tumor necrosis factor (TNF) is considered an adverse mediator of heat stroke (HS) based on clinical studies showing high serum levels. However, soluble TNF receptors (sTNFR; TNF antagonists) were higher in survivors than nonsurvivors, and TNFR knockout (KO) mice showed a trend toward increased mortality, suggesting TNF has protective actions for recovery. We delineated TNF actions in HS by comparing thermoregulatory, metabolic, and inflammatory responses between B6129F2 (wild type, WT) and TNFR KO mice. Before heat exposure, TNFR KO mice showed ~0.4°C lower core temperature (T(c); radiotelemetry), ~10% lower metabolic rate (M(r); indirect calorimetry), and reduced plasma interleukin (IL)-1α and sIL-1RI than WT mice. KO mice selected warmer temperatures than WT mice in a gradient but remained hypothermic. In the calorimeter, both genotypes showed a similar heating rate, but TNFR KO maintained lower T(c) and M(r) than WT mice for a given heat exposure duration and required ~30 min longer to reach maximum T(c) (42.4°C). Plasma IL-6 increased at ~3 h of recovery in both genotypes, but KO mice showed a more robust sIL-6R response. Higher sIL-6R in the KO mice was associated with delayed liver p-STAT3 protein expression and attenuated serum amyloid A3 (SAA3) gene expression, suggesting the acute phase response (APR) was attenuated in these mice. Our data suggest that the absence of TNF signaling induced a regulated hypothermic state in the KO mice, TNF-IL-1 interactions may modulate T(c) and M(r) during homeostatic conditions, and TNF modulates the APR during HS recovery through interactions with the liver IL-6-STAT3 pathway of SAA3 regulation.

Modeling cortisol dynamics in the neuro-endocrine axis distinguishes normal, depression, and post-traumatic stress disorder (PTSD) in humans.

Cortisol, secreted in the adrenal cortex in response to stress, is an informative biomarker that distinguishes anxiety disorders such as major depression and post-traumatic stress disorder (PTSD) from normal subjects. Yehuda et al. proposed a hypothesis that, in humans, the hypersensitive hypothalamus-pituitary-adrenal (HPA) axis is responsible for the occurrence of differing levels of cortisol in anxiety disorders. Specifically, PTSD subjects have lower cortisol levels during the late subjective night in comparison to normal subjects, and this was assumed to occur due to strong negative feedback loops in the HPA axis. In the present work, to address this hypothesis, we modeled the cortisol dynamics using nonlinear ordinary differential equations and estimated the kinetic parameters of the model to fit the experimental data of three categories, namely, normal, depressed, and PTSD human subjects. We concatenated the subjects (n = 3) in each category and created a model subject (n = 1) without considering the patient-to-patient variability in each case. The parameters of the model for the three categories were simultaneously obtained through global optimization. Bifurcation analysis carried out with the optimized parameters exhibited two supercritical Hopf points and, for the choice of parameters, the oscillations were found to be circadian in nature. The fitted kinetic parameters indicate that PTSD subjects have a strong negative feedback loop and, as a result, the predicted oscillating cortisol levels are extremely low at the nadir in contrast to normal subjects, albeit within the endocrinologic range. We also simulated the phenotypes for each of the categories and, as observed in the clinical data of PTSD patients, the simulated cortisol levels are consistently low at the nadir, and correspondingly the negative feedback was found to be extremely strong. These results from the model support the hypothesis that high stress intensity and strong negative feedback loop may cause hypersensitive neuro-endocrine axis that results in hypocortisolemia in PTSD.

Understanding the dosing-time-dependent antihypertensive effect of valsartan and aspirin through mathematical modeling.

Chronopharmacology of arterial hypertension impacts the long-term cardiovascular risk of hypertensive subjects. Therefore, clinical and computational studies have proposed optimizing antihypertensive medications' dosing time (Ta). However, the causes and mechanisms underlying the Ta-dependency antihypertensive effect have not been elucidated. Here we propose using a Ta- dependent effect model to understand and predict the antihypertensive effect of valsartan and aspirin throughout the day in subjects with grade I or II essential hypertension. The model based on physiological regulation mechanisms includes a periodic function for each parameter that changes significantly after treatment. Circadian variations of parameters depending on the dosing time allowed the determination of regulation mechanisms dependent on the circadian rhythm that were most relevant for the action of each drug. In the case of valsartan, it is the regulation of vasodilation and systemic vascular resistance. In the case of aspirin, the antithrombotic effect generates changes in the sensitivity of systemic vascular resistance and heart rate to changes in physical activity. Dosing time-dependent models predict a more significant effect on systemic vascular resistance and blood pressure when administering valsartan or aspirin at bedtime. However, circadian dependence on the regulation mechanisms showed different sensitivity of their circadian parameters and shapes of functions, presenting different phase shifts and amplitude. Therefore, different mechanisms of action and pharmacokinetic properties of each drug can generate different profiles of Ta-dependence of antihypertensive effect and optimal dosing times.

A Probabilistic Approach to Blood Glucose Prediction in Type 1 Diabetes Under Meal Uncertainties.

Currently, most reliable and commercialized artificial pancreas systems for type 1 diabetes are hybrid closed-loop systems, which require the user to announce every meal and its size. However, estimating the amount of carbohydrates in a meal and announcing each and every meal is an error-prone process that introduces important uncertainties to the problem, which when not considered, lead to sub-optimal outcomes of the controller. To address this problem, we propose a novel deep-learning-based model for probabilistic glucose prediction, called the Input and State Recurrent Kalman Network (ISRKN), which consists in the incorporation of an input and state Kalman filter in the latent space of a deep neural network so that the posterior distributions can be computed in closed form and the uncertainty can be propagated using the Kalman equations. In addition, the proposed architecture allows explicit estimation of the meal uncertainty distribution, whose parameters are encoded in the filter parameters. Results using the UVA/Padova simulator and data from a clinical trial show that the proposed model outperforms other probabilistic models using several probabilistic metrics across different degrees of distributional shifts.

Increased respiratory modulation of cardiovascular control reflects improved blood pressure regulation in pregnancy.

Hypertensive pregnancy disorders put the maternal-fetal dyad at risk and are one of the leading causes of morbidity and mortality during pregnancy. Multiple efforts have been made to understand the physiological mechanisms behind changes in blood pressure. Still, to date, no study has focused on analyzing the dynamics of the interactions between the systems involved in blood pressure control. In this work, we aim to address this question by evaluating the phase coherence between different signals using wavelet phase coherence. Electrocardiogram, continuous blood pressure, electrocardiogram-derived respiration, and muscle sympathetic nerve activity signals were obtained from ten normotensive pregnant women, ten normotensive non-pregnant women, and ten pregnant women with preeclampsia during rest and cold pressor test. At rest, normotensive pregnant women showed higher phase coherence in the high-frequency band (0.15-0.4 Hz) between muscle sympathetic nerve activity and the RR interval, blood pressure, and respiration compared to non-pregnant normotensive women. Although normotensive pregnant women showed no phase coherence differences with respect to hypertensive pregnant women at rest, higher phase coherence between the same pairs of variables was found during the cold pressor test. These results suggest that, in addition to the increased sympathetic tone of normotensive pregnant women widely described in the existing literature, there is an increase in cardiac parasympathetic modulation and respiratory-driven modulation of muscle sympathetic nerve activity and blood pressure that could compensate sympathetic increase and make blood pressure control more efficient to maintain it in normal ranges. Moreover, blunted modulation could prevent its buffer effect and produce an increase in blood pressure levels, as observed in the hypertensive women in this study. This initial exploration of cardiorespiratory coupling in pregnancy opens the opportunity to follow up on more in-depth analyses and determine causal influences.

Maximal pulmonary ventilation and lactate affect the anaerobic performance in young women exposed to hypobaric hypoxia.

Background: Athletes, tourists, and mining workers from all over the world ascend daily to an altitude greater than 3.000 meters above sea level to perform different activities, all of which demand physical effort. A ventilation increase is the first mechanism once the chemoreceptors perceive hypoxia, and is key to maintaining blood oxygen levels during acute exposure to high altitudes and to buffering lactic acidosis during exercise. It has been observed that gender is a variable that can influence the ventilatory response. Still, the available literature is limited due to the few studies considering women as study subjects. The influence of gender on anaerobic performance and its effects under high altitudes (HA) environments have been poorly studied. Objective: The objectives of this study were to evaluate anaerobic performance in young women exposed to high altitudes and to compare the physiological response to multiple sprints between women and men measured by ergospirometry. Methodology: Nine women and nine men (22.9 ± 3.2 years old) carried out the multiple-sprint anaerobic tests under two conditions, sea level and high altitudes. Results: In the first 24 h of exposure to a high altitudes, lactate levels were higher in women than those in men (2.57 ± 0.4 Mmol/L, 2.18 ± 0.3 Mmol/L, respectively; p < 0.05). Second, women had a decreased ventilatory response in exposure to high altitudes compared to men (p > 0.005). Third, there is a positive correlation between lactate levels prior to an anaerobic test and the ventilatory response developed by subjects at high altitudes (R2 = 0.33, slope = -41.7, and p < 0.05). Lastly, this ventilatory response can influence VO2peak (R2 = 0.60, slope = 0.02, and p < 0.001). Conclusion: This study provides insights into the mechanisms behind the reduced respiratory capacity observed in women during an anaerobic exercise test at high altitudes. An acute response to HA showed a greater work of breathing and increased the drive ventilatory response. It is possible to postulate the differences in the fatigue-induced metaboreflex of the respiratory muscles and aerobic-anaerobic transition between genders. These results on multiple sprint performance and the influences of gender in hypoxic environments deserve further investigation.

Self-regulation learning as active inference: dynamic causal modeling of an fMRI neurofeedback task.

Introduction: Learning to self-regulate brain activity by neurofeedback has been shown to lead to changes in the brain and behavior, with beneficial clinical and non-clinical outcomes. Neurofeedback uses a brain-computer interface to guide participants to change some feature of their brain activity. However, the neural mechanism of self-regulation learning remains unclear, with only 50% of the participants succeeding in achieving it. To bridge this knowledge gap, our study delves into the neural mechanisms of self-regulation learning via neurofeedback and investigates the brain processes associated with successful brain self-regulation. Methods: We study the neural underpinnings of self-regulation learning by employing dynamical causal modeling (DCM) in conjunction with real-time functional MRI data. The study involved a cohort of 18 participants undergoing neurofeedback training targeting the supplementary motor area. A critical focus was the comparison between top-down hierarchical connectivity models proposed by Active Inference and alternative bottom-up connectivity models like reinforcement learning. Results: Our analysis revealed a crucial distinction in brain connectivity patterns between successful and non-successful learners. Particularly, successful learners evinced a significantly stronger top-down effective connectivity towards the target area implicated in self-regulation. This heightened top-down network engagement closely resembles the patterns observed in goal-oriented and cognitive control studies, shedding light on the intricate cognitive processes intertwined with self-regulation learning. Discussion: The findings from our investigation underscore the significance of cognitive mechanisms in the process of self-regulation learning through neurofeedback. The observed stronger top-down effective connectivity in successful learners indicates the involvement of hierarchical cognitive control, which aligns with the tenets of Active Inference. This study contributes to a deeper understanding of the neural dynamics behind successful self-regulation learning and provides insights into the potential cognitive architecture underpinning this process.

Quality of life, exercise capacity, cognition, and mental health of Chilean patients after COVID-19: an experience of a multidisciplinary rehabilitation program at a physical and rehabilitation medicine unit.

Background: Post-COVID disabilities, encompassing physical, cognitive, and psychological aspects, constitute the primary health sequelae for survivors. While the rehabilitation needs post COVID-19 are now well understood, each country possesses unique characteristics in terms of populations, healthcare systems, social dynamics, and economic profiles, necessitating context-specific recommendations. This study aims to address two main objectives: (1) analyze the impact of an 8-week multidisciplinary rehabilitation program on the quality of life, functional capacity, cognition, and mental health adaptations in adults recovering from COVID-19 in northern Chile, and (2) propose a personalized model for predicting program dropouts and responses. Methods: A total of 44 subjects were enrolled, forming two groups during the study: a treatment group (n = 32) and a dropout group (n = 12). The treatment group participated in the 8-week multidisciplinary rehabilitation program. Results: The results indicate that (1) After 8 weeks, the quality of life of the patients in the treatment group exhibited significant improvements reflected in all aspects of the Short Form-36 Health Survey (SF36, p < 0.005) and the total score (p < 0.001), with a concurrent decrease in dysfunctionality (p < 0.001). (2) Significant improvements were also observed in various physical performance tests, including the: 6-minute walk test, 1-min sit-to-stand, dynamometry, Tinetti balance, and Berg score (p < 0.001). Moreover, physical therapy led to a reduction in neuropathic symptoms and pain, psychological therapy reduced anxiety and depression, and language therapy enhanced memory and speech (all p < 0.05). (3) Demographic and clinical history characteristics did not predict responses to rehabilitation. (4) A regression model for predicting changes in SF-36 total score, based on physical function, physical role, general health, and mental health, was established based on the data from study (p < 0.01, adjusted R2 = 0.893). (5) Classification models for predicting dropouts achieved 68% accuracy, with key predictors of treatment adherence including diabetes, hypertension, and dyslipidemia, Tinetti balance, physical role, and vitality of SF36, and performance on the 6-minute walk test and 1-minute sit-to-stand. Conclusions: This study demonstrates significant enhancements in quality of life, improved functional performance, and reductions in mental and cognitive burdens within an 8-week rehabilitation program. Additionally, it is possible to identify patients at risk of dropping out using cost-effective, outpatient, and clinically applicable tests.

Effect of Primary Packaging Material on the Stability Characteristics of Diazepam and Midazolam Parenteral Formulations.

Diazepam and midazolam are formulated in autoinjectors for parenteral administration to decrease seizures in the case of emergency. However, the compatibility of these lipophilic drugs with the primary packaging material is a key part of drug formulation development. In this work, diazepam and midazolam were packaged in glass syringes as parenteral solutions using two different elastomeric sealing materials (PH 701/50 C BLACK and 4023/50 GRAY). Syringes were stored at three different storage temperatures: 4, 25, and 40 °C. At different time points over 3 years, physical appearance, benzodiazepine sorption on the sealing elastomeric materials, and drug content in solution were assayed. A detailed study on the adsorption profile of both benzodiazepines on the elastomeric gaskets was performed, indicating that the novel rubber material made of bromobutyl derivatives (4023/50 GRAY) is a better choice for manufacturing autoinjectors due to lower drug adsorption. Diazepam showed a better stability profile than midazolam, with the latter solubilised as a hydrochloride salt in an acidic pH that can affect the integrity of the elastomer over time. The amount of drug adsorbed on the surface of the elastomer was measured by NIR and correlated using chemometric models with the amount retained in the elastomeric gaskets quantified by HPLC.

SensSB: a software toolbox for the development and sensitivity analysis of systems biology models.

SUMMARY: SensSB (Sensitivity Analysis for Systems Biology) is an easy to use, MATLAB-based software toolbox, which integrates several local and global sensitivity methods that can be applied to a wide variety of biological models. In addition to addressing the sensitivity analysis problem, SensSB aims to cover all the steps involved during the modeling process. The main features of SensSB are: (i) derivative and variance-based global sensitivity analysis, (ii) pseudo-global identifiability analysis, (iii) optimal experimental design (OED) based on global sensitivities, (iv) robust parameter estimation, (v) local sensitivity and identifiability analysis, (vi) confidence intervals of the estimated parameters and (vii) OED based on the Fisher Information Matrix (FIM). SensSB is also able to import models in the Systems Biology Mark-up Language (SBML) format. Several examples from simple analytical functions to more complex biological pathways have been implemented and can be downloaded together with the toolbox. The importance of using sensitivity analysis techniques for identifying unessential parameters and designing new experiments is quantified by increased identifiability metrics of the models and decreased confidence intervals of the estimated parameters. AVAILABILITY: SensSB is a software toolbox freely downloadable from http://www.iim.csic.es/ approximately gingproc/SensSB.html. The web site also contains several examples and an extensive documentation. CONTACT: mrodriguez@iim.csic.es SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Vulnerabilities in the tau network and the role of ultrasensitive points in tau pathophysiology.

The multifactorial nature of disease motivates the use of systems-level analyses to understand their pathology. We used a systems biology approach to study tau aggregation, one of the hallmark features of Alzheimer's disease. A mathematical model was constructed to capture the current state of knowledge concerning tau's behavior and interactions in cells. The model was implemented in silico in the form of ordinary differential equations. The identifiability of the model was assessed and parameters were estimated to generate two cellular states: a population of solutions that corresponds to normal tau homeostasis and a population of solutions that displays aggregation-prone behavior. The model of normal tau homeostasis was robust to perturbations, and disturbances in multiple processes were required to achieve an aggregation-prone state. The aggregation-prone state was ultrasensitive to perturbations in diverse subsets of networks. Tau aggregation requires that multiple cellular parameters are set coordinately to a set of values that drive pathological assembly of tau. This model provides a foundation on which to build and increase our understanding of the series of events that lead to tau aggregation and may ultimately be used to identify critical intervention points that can direct the cell away from tau aggregation to aid in the treatment of tau-mediated (or related) aggregation diseases including Alzheimer's.