Miracle-dz/UniFAR-FTUs huggingface dataset:
Age-related differences in prefrontal glutamate are associated with increased working memory decay that gives the appearance of learning deficits

The ability to use past experience to effectively guide decision making declines in older adulthood. Such declines have been theorized to emerge from either impairments of striatal reinforcement learning systems (RL) or impairments of recurrent networks in prefrontal and parietal cortex that support working memory (WM). Distinguishing between these hypotheses has been challenging because either RL or WM could be used to facilitate successful decision making in typical laboratory tasks. Here we investigated the neurocomputational correlates of age-related decision making deficits using an RL-WM task to disentangle these mechanisms, a computational model to quantify them, and magnetic resonance spectroscopy to link them to their molecular bases. Our results reveal that task performance is worse in older age, in a manner best explained by working memory deficits, as might be expected if cortical recurrent networks were unable to sustain persistent activity across multiple trials. Consistent with this, we show that older adults had lower levels of prefrontal glutamate, the excitatory neurotransmitter thought to support persistent activity, compared to younger adults. Individuals with the lowest prefrontal glutamate levels displayed the greatest impairments in working memory after controlling for other anatomical and metabolic factors. Together, our results suggest that lower levels of prefrontal glutamate may contribute to failures of working memory systems and impaired decision making in older adulthood.
Machine Learning based Information Forensics from Smart Sources

We live in a world that connects, socializes and interacts using internet. Humans generate tons of information on daily basis, according to Forbes 2.5 quintillion bytes of data is created each day in year 2018. The data creation pace is continuously accelerating with the growth of the Internet of Things (IoT). Extensive social media usage fuels data creation which is primarily generated from mobile phones. In the present scenario, data is the asset and this asset is extremely vulnerable. In our research work we utilized these data sources to aid digital forensics investigation. Due to our technology engulfed lifestyle, we leave a lot of information about ourselves during our routine activities. These traces are used by the wrongdoers for their vicious objectives but also these traces can be used by investigators to understand any incident and to penalize the delinquents. Forensics investigators face challenges with a huge amount of data during investigations. Whether the data source is an online social network, a smart phone or an IOT based environment, huge amount of data adds complexity and delay to forensics investigation. To contribute to the forensics investigation we propose the use of machine learning for forensics data anaylsis. Forensics investigation is a three-phase process including data acquisition, data analysis and presentation. Our research focuses on the first two phases of the forensics investigation cycle i.e. data collection and data analysis. This thesis discusses following research achievements: 1. Data acquisition from smart sources for forensic information especially for IoT 2. Machine learning based data analysis to extract forensic artefacts 3. IoT forensics framework(acquisition and analysis phase) implementation This thesis is segmented based on the three data sources for data analysis namely online social networks, smart phones and sensor-based networks (IoT). Using IoT based data this thesis proposes a scheme SACIFS(Smart aged care information forensics) for IoT forensic feature extraction and data analysis of elderly patients monitored in a nursing home environment. We developed our machine learning model based on Support Vector Machine to detect an incident and highlight relevant forensic artefacts. We used smart phone data in Digital Forensic Intelligence Analysis Cycle framework to identify strongly connected contacts during triage phase. We classified the contacts of a smart phone user with respect to their closeness, extracted from the data features from Facebook messenger. Moreover, utilizing publically available Online Social Network, we analysed multiple tools to collect, analyse and visualize data from Facebook pages and groups.
Cromolyn prevents cerebral vasospasm and dementia by targeting WDR43

Background Cerebral vasospasm (CV) can cause inflammation and damage to neuronal cells in the elderly, leading to dementia. Purpose This study aimed to investigate the genetic mechanisms underlying dementia caused by CV in the elderly, identify preventive and therapeutic drugs, and evaluate their efficacy in treating neurodegenerative diseases. Methods Genes associated with subarachnoid hemorrhage and CV were acquired and screened for differentially expressed miRNAs (DEmiRNAs) associated with aneurysm rupture. A regulatory network of DEmiRNAs and mRNAs was constructed, and virtual screening was performed to evaluate possible binding patterns between Food and Drug Administration (FDA)-approved drugs and core proteins. Molecular dynamics simulations were performed on the optimal docked complexes. Optimally docked drugs were evaluated for efficacy in the treatment of neurodegenerative diseases through cellular experiments. Results The study found upregulated genes (including WDR43 and THBS1) and one downregulated gene associated with aneurysm rupture. Differences in the expression of these genes indicate greater disease risk. DEmiRNAs associated with ruptured aortic aneurysm were identified, of which two could bind to THBS1 and WDR43. Cromolyn and lanoxin formed the best docking complexes with WDR43 and THBS1, respectively. Cellular experiments showed that cromolyn improved BV2 cell viability and enhanced Aβ42 uptake, suggesting its potential as a therapeutic agent for inflammation-related disorders. Conclusion The findings suggest that WDR43 and THBS1 are potential targets for preventing and treating CV-induced dementia in the elderly. Cromolyn may have therapeutic value in the treatment of Alzheimer’s disease and dementia.
Amino acids integrate behaviors in nerveless placozoans

Placozoans are the simplest known free-living animals without recognized neurons and muscles but a complex behavioral repertoire. However, mechanisms and cellular bases of behavioral coordination are unknown. Here, using Trichoplax adhaerens as a model, we described 0.02–0.002 Hz oscillations in locomotory and feeding patterns as evidence of complex multicellular integration; and showed their dependence on the endogenous secretion of signal molecules. Evolutionary conserved low-molecular-weight transmitters (glutamate, aspartate, glycine, GABA, and ATP) acted as coordinators of distinct locomotory and feeding patterns. Specifically, L-glutamate induced and partially mimicked endogenous feeding cycles, whereas glycine and GABA suppressed feeding. ATP-modified feeding is complex, first causing feeding-like cycles and then suppressing feeding. Trichoplax locomotion was modulated by glycine, GABA, and, surprisingly, by animals’ own mucus trails. Mucus triples locomotory speed compared to clean substrates. Glycine and GABA increased the frequency of turns. The effects of the amino acids are likely mediated by numerous receptors (R), including those from ionotropic GluRs, metabotropic GluRs, and GABA-BR families. Eighty-five of these receptors are encoded in the Trichoplax genome, more than in any other animal sequenced. Phylogenetic reconstructions illuminate massive lineage-specific expansions of amino acid receptors in Placozoa, Cnidaria, and Porifera and parallel evolution of nutritional sensing. Furthermore, we view the integration of feeding behaviors in nerveless animals by amino acids as ancestral exaptations that pave the way for co-options of glutamate, glycine, GABA, and ATP as classical neurotransmitters in eumetazoans.
Understanding Health-Related Quality of Life in Kidney Transplant Recipients: The Role of Symptom Experience and Illness Perceptions

The purpose of our article is to investigate the impact of symptom experience on health related quality of life (HRQOL) in kidney transplant recipients (KTRs) and whether illness perceptions mediated this impact. Symptom experience, illness perceptions, and HRQOL were measured at transplantation and 6 weeks after transplantation in KTRs in an ongoing Dutch cohort study. Multivariable linear regression models were used for the analysis. 90 KTRs were analyzed. Fatigue and lack of energy were the most prevalent and burdensome symptoms at transplantation. Mental HRQOL at 6 weeks after transplantation was comparable to that of the general Dutch population (mean [standard deviation, SD]: 49.9 [10.7]) versus 50.2 [9.2]), while physical HRQOL was significantly lower (38.9 [9.1] versus 50.6 [9.2]). Experiencing more symptoms was associated with lower physical and mental HRQOL, and the corresponding HRQOL reduced by −0.15 (95%CI, −0.31; 0.02) and −0.23 (95%CI, −0.42; −0.04) with each additional symptom. The identified mediation effect suggests that worse symptom experiences could cause more unhelpful illness perceptions and consequently lead to lower HRQOL. Illness perceptions may explain the negative impact of symptom experience on HRQOL. Future studies at later stages after kidney transplantation are needed to further explore the mediation effect of illness perceptions and guide clinical practice to improve HRQOL. Graphical Abstract
Strategies for Improving Postpartum Contraception Compared With Routine Maternal Care: A Systematic Review and Meta-Analysis

Objectives: This study aimed to systematically review the effectiveness of service interventions for improving postpartum contraception, including contraceptive use, prevention of repeat pregnancies and induced abortions. Methods: A systematic literature search was conducted in three databases until June 2022 (PROSPERO registration CRD42022328349). Estimates of intervention effects from meta-analyses were represented as odds ratios (OR) with 95% confidence intervals (CI). Results: 16 studies with 14,289 participants were included, with four kinds of interventions recognized. Interventions effect in increasing use of contraceptives and decreasing rates of repeated pregnancy for up to 6 months postpartum (OR = 2.24, 0.06, 95% CI = 1.46–3.44, 0.02–0.22, respectively), with no significant associations with contraceptive use at 12 months postpartum, prevention of postpartum repeat pregnancies and induced abortions during 1 year after childbirth. Conclusion: We concluded that interventions impact the initiation of postpartum contraceptive use and prevention of repeat pregnancy with an overall certainty from low to moderate. These findings highlight the need for additional studies to integrate the beneficial effect of several interventions and then design more feasible strategies, which is important for the maternal and child healthcare systems.
Predicting therapeutic efficacy of oral rehydration salts in children with vasovagal syncope

Objective This study was designed to develop an easy-to-perform and inexpensive measure to predict efficacy of the oral rehydration salts (ORS) in children with vasovagal syncope (VVS). Materials and methods Children diagnosed with VVS and treated with ORS for a median of 3 months at the Peking University First Hospital, China, were enrolled and followed up. Demographic data, clinical hemodynamic parameters, and variables related to red blood cells were collected at the baseline. On the basis of changes in symptom scores after treatment, participants were divided into effective or ineffective groups at the end of the follow-up. Logistic regression analysis was used to investigate parameters related to therapeutic efficacy of ORS and a predictive model of ORS effectiveness was created. The predictive efficiency was evaluated using the receiver operating characteristic curve. The accuracy/consistency was evaluated by the Hosmer–Lemeshow test and calibration curve. Internal validation was done using the bootstrap approach. Results Totally 97 pediatric participants were included in the study and 4 (4.1%) were lost during the follow-up. ORS therapy was effective in 46 children and ineffective in 47 children. Children in the effective group had higher baseline red blood cell count, hemoglobin, and hematocrit than those in the ineffective group (p < 0.01). Through logistic regression analysis, the baseline hematocrit and body mass index (BMI) were included in predictive model for the response to ORS treatment. The predictive efficacy of the model showed an area under the curve of 0.77 (p < 0.01). The predicted probability cut-off value of 0.5 was found to be optimal, with a resulting sensitivity of 67.4% and specificity of 80.9%. In the Hosmer–Lemeshow test, p-value was 0.75, and the calibration plot showed a good model fitness. Internal validation was performed using the bootstrap approach (n = 1,000), showing 95% confidence interval of 0.67–0.86. Conclusion Hemoglobin combined with BMI was useful for predicting the therapeutic efficacy of ORS in children with VVS.
Modeling the impact of digital readiness in recruiting and sustaining underrepresented groups: Data from the All of Us research program

The All of Us Research Program (All of Us or Program) is an ongoing longitudinal data collection operated by the National Institutes of Health (NIH). The Program aims to improve healthcare for all through the development of a biomedical research resource reflective of the diversity of the United States that includes Underrepresented in Biomedical Research (UBR) groups. Federally Qualified Health Centers (FQHCs) are a key recruitment stream of UBR participants, which are community based and provide primary care and preventive services in medically underserved areas. Over 90% of FQHC patients enrolled in All of Us to date are UBR. The COVID-19 pandemic caused a pause in All of Us activities. Re-starting the activities was a challenge, especially due to the digital divide faced by FQHC participants, and that most Program activities are primarily completed via web-based portal from a computer or a mobile device. This paper investigates the extent to which digital readiness impacted recruitment and sustainment of a pre-pandemic sample of 2,791 FQHC participants to the Program. Digital readiness was defined by access to home-based or other internet-accessing devices, and participants’ comfort level using such devices. Results from multivariable logistic regression models showed that lower age, more education, female gender identity, and higher income were associated with higher digital readiness (p ≤ 0.01). Race, rurality, and sexual orientation status were not significant factors associated with digital readiness. Older participants had higher odds of completing Program activities, even though less digitally ready than their younger peers, as they often completed the activities during their in-person clinical visits. A subsequent weighted model demonstrated that FQHC participants who were digitally ready had 27% higher odds of completing Program activities than those not digitally ready. The data highlight the need for improved connectivity and sustainment between longitudinal data collection, research programs, and UBR participants, particularly among those facing the digital divide. Quantifying digital challenges provide operational insights for longitudinal data collection (All of Us, or others), and broadly, other aspects of digital medicine such as telehealth or patient portals by recognizing digital readiness of participants and patients, and the level of support required for success.
Deep brain stimulation for the treatment of Alzheimer's disease: A systematic review and meta-analysis

Background One of the experimental neuromodulation techniques being researched for the treatment of Alzheimer's disease (AD) is deep brain stimulation (DBS). To evaluate the effectiveness of DBS in AD, we performed a systematic review and meta-analysis of the available evidence. Methods From the inception through December 2021, the following databases were searched: Medline via PubMed, Scopus, Embase, Cochrane Library, and Web of Science. The search phrases used were “Alzheimer's disease,” “AD,” “deep brain stimulation,” and “DBS. ” The information from the included articles was gathered using a standardized data-collecting form. In the included papers, the Cochrane Collaboration methodology was used to evaluate the risk of bias. A fixed-effects model was used to conduct the meta-analysis. Results Only five distinct publications and 6 different comparisons (one study consisted of two phases) were included out of the initial 524 papers that were recruited. DBS had no impact on the cognitive ability in patients with AD [0.116 SMD, 95% confidence interval (CI), −0.236 to 0.469, p = 0.518]. The studies' overall heterogeneity was not significant (κ2 = 6.23, T2 = 0.053, df = 5, I2 = 19.76%, p = 0.284). According to subgroup analysis, the fornix-DBS did not improve cognitive function in patients with AD (0.145 SMD, 95%CI, −0.246 to 0.537, p = 0.467). Unfavorable neurological and non-neurological outcomes were also reported. Conclusion The inconsistencies and heterogeneity of the included publications in various target and age groups of a small number of AD patients were brought to light by this meta-analysis. To determine if DBS is useful in the treatment of AD, further studies with larger sample sizes and randomized, double-blinded, sham-controlled designs are required.
Dynamics of a model for the degradation mechanism of aggregated α-synuclein in Parkinson's disease

Accumulation of the misfolded synaptic protein α-synuclein (αSyn*) is a hallmark of neurodegenerative disease in Parkinson's disease (PD). Recent studies suggest that the autophagy lysosome pathway (ALP) including both the Beclin1-associated and mTOR-signaling pathways is involved in the αSyn* clearance mechanism. In this study, a mathematical model is proposed for the degradation of αSyn* by ALP with the crosstalk element of mTOR. Using codimension-1 bifurcation analysis, the tri-stability of αSyn* is surveyed under three different stress signals and, in addition, consideration is given to the regulatory mechanisms for the Beclin1- and mTOR-dependent rates on αSyn* degradation using the codimension-1 and−2 bifurcation diagrams. It was found that, especially under internal and external oxidative stresses (S1), the bistable switch of the aggregation of αSyn* can be transformed from an irreversible to a reversible condition through the ALP degradation pathways. Furthermore, the robustness of the tri-stable state for the stress S1 to the parameters related to mTOR-mediated ALP was probed. It was confirmed that mTOR-mediated ALP is important for maintaining the essential dynamic features of the tri-stable state. This study may provide a promising avenue for conducting further experiments and simulations of the degradation mechanism of dynamic modeling in PD.
Combined multidisciplinary in/outpatient rehabilitation delays definite nursing home admission in advanced Parkinson’s disease patients

Introduction Advanced Parkinson’s disease (aPD) patients have a high risk on definite nursing home admission. We analyzed the effectiveness of an in-and outpatient multidisciplinary rehabilitation, focusing on activities of daily living (ADL) and delaying definite nursing home admission. Methods This study included 24 aPD patients, who received a 6-week inpatient multidisciplinary rehabilitation program, including optimization of pharmacotherapy, which was followed by an individualized outpatient support program during 2 years (intervention group). A non-randomized matched control group (n = 19), received care as usual. Primary endpoints consisted of the Amsterdam Linear Disability Scale (ALDS) and percentage of patients being able to live independently at home after 2 years. Secondary endpoints included changes in medication (LEDD), motor performance (SCOPA-SPES), cognition (SCOPA-COG), hallucinations (NPI) and depression (BDI). Results Overall, 83% of patients were able to return home after the 6-week inpatient intervention, and 65% still lived at home at 2 years follow-up. Median ALDS scores after 2 years in the intervention group were significantly better, compared to the control group (p = 0.002). All secondary endpoints had improved significantly vs. baseline directly after the 6-week inpatient rehabilitation, which had disappeared at 2 years follow-up, with the exception of the daily dose of medication, which was significantly higher in the intervention group. Conclusion This 2-year follow-up study showed that a combined multidisciplinary in/outpatient rehabilitation program for aPD patients, was able to stabilize ADL functions, and finally delayed definite nursing home admissions in 65% of treated patients. Trial registration filenumber M10.091051; ABR code NL32699.042. 10.
Factors associated with the incidence and the expenditure of self-medication among middle-aged and older adults in China: A cross-sectional study

Background With the accelerated ageing of population and the growing prevalence of various chronic diseases in China, self-medication plays an increasingly important role in complementing the health care system due to its convenience and economy. Objective This study aimed to investigate the incidence of self-medication and the amount of self-medication expenditure among middle-aged and older adults in China, and to explore factors associated with them. Methods A total of 10,841 respondents aged 45 years and older from the China Health and Retirement Longitudinal Study (CHARLS) wave 4 which conducted in 2018 were included as the sample of this study. The two-part model was adopted to identify the association between the incidence of self-medication and the amount of self-medication expenditure and specific factors, respectively. Results The incidence of self-medication among Chinese middle-aged and older adults was 62.30%, and the average total and out-of-pocket (OOP) pharmaceutical expenditure of self-medication of the self-medicated individuals were 290.50 and 264.38 Chinese yuan (CNY) respectively. Participants who took traditional Chinese medicine (TCM), self-reported fair, and poor health status, suffered from one and multiple chronic diseases had strongly higher incidence of self-medication. Older age and multiple chronic diseases were strongly associated with higher expenditure of self-medication. Those who took TCM had more self-medication expenditure, while those who drank alcohol had less. Conclusion Our study demonstrated the great prevalence of self-medication among middle-aged and older adults in China and the large pharmaceutical expenditure that come with it, especially in the high-risk groups of self-medication identified in this paper. These findings enhanced our understanding of self-medication behaviors among Chinese middle-aged and older adults and may contribute to the formulation of targeted public health policy.