Neural Network-Based Li-Ion Battery Aging Model at Accelerated C-Rate

Lithium-ion (Li-ion) batteries are widely used in electric vehicles (EVs) because of their high energy density, low self-discharge, and superior performance. Despite this, Li-ion batteries’ performance and reliability become critical as they lose their capacity with increasing charge and discharging cycles. Moreover, Li-ion batteries are subject to aging in EVs due to load variations in discharge. Monitoring the battery cycle life at various discharge rates would enable the battery management system (BMS) to implement control parameters to resolve the aging issue. In this paper, a battery lifetime degradation model is proposed at an accelerated current rate (C-rate). Furthermore, an ideal lifetime discharge rate within the standard C-rate and beyond the C-rate is proposed. The consequence of discharging at an accelerated C-rate on the cycle life of the batteries is thoroughly investigated. Moreover, the battery degradation model is investigated with a deep learning algorithm-based feed-forward neural network (FNN), and a recurrent neural network (RNN) with long short-term memory (LSTM) layer. A comparative assessment of performance of the developed models is carried out and it is shown that the LSTM-RNN battery aging model has superior performance at accelerated C-rate compared to the traditional FNN network

A cross-sectional study confirms temporary post-COVID-19 vaccine menstrual irregularity and the associated physiological changes among vaccinated women in Jordan

BackgroundCOVID-19 vaccines continue to save people’s lives around the world; however, some vaccine adverse events have been a major concern which slowed down vaccination campaigns. Anecdotal evidence pointed to the vaccine effect on menstruation but evidence from the adverse event reporting systems and the biomedical literature was lacking. This study aimed to investigate the physiological changes in women during menstruation amid the COVID-19 vaccination.MethodsA cross-sectional online survey was distributed to COVID-19 vaccinated women from Nov 2021 to Jan 2022. The results were analyzed using the SPSS software.ResultsAmong the 564 vaccinated women, 52% experienced significant menstrual irregularities post-vaccination compared to before regardless of the vaccine type. The kind of menstrual irregularity varied among the vaccinated women, for example, 33% had earlier menstruation, while 35% reported delayed menstruation. About 31% experienced heavier menstruation, whereas 24% had lighter menstrual flow. About 29% had menstruation last longer, but 13% had it shorter than usual. Noteworthy, the menstrual irregularities were more frequent after the second vaccine shot, and they disappeared within 3 months on average. Interestingly, 24% of the vaccinated women reported these irregularities to their gynecologist.ConclusionThe COVID-19 vaccine may cause physiological disturbances during menstruation. Luckily, these irregularities were short-termed and should not be a reason for vaccine hesitancy in women. Further studies are encouraged to unravel the COVID-19 vaccine adverse effect on women’s health

Sitagliptin: a potential drug for the treatment of COVID-19?

Recently, an outbreak of a fatal coronavirus, SARS-CoV-2, has emerged from China and is rapidly spreading worldwide. Possible interaction of SARS-CoV-2 with DPP4 peptidase may partly contribute to the viral pathogenesis. An integrative bioinformatics approach starting with mining the biomedical literature for high confidence DPP4-protein/gene associations followed by functional analysis using network analysis and pathway enrichment was adopted. The results indicate that the identified DPP4 networks are highly enriched in viral processes required for viral entry and infection, and as a result, we propose DPP4 as an important putative target for the treatment of COVID-19. Additionally, our protein-chemical interaction networks identified important interactions between DPP4 and sitagliptin. We conclude that sitagliptin may be beneficial for the treatment of COVID-19 disease, either as monotherapy or in combination with other therapies, especially for diabetic patients and patients with pre-existing cardiovascular conditions who are already at higher risk of COVID-19 mortality

In silico strategies to study polypharmacology of G-protein-coupled receptors

The development of drugs that simultaneously target multiple receptors in a rational way (i.e., 'magic shotguns') is regarded as a promising approach for drug discovery to treat complex, multi-factorial and multi-pathogenic diseases. My major goal is to develop and employ different computational approaches towards the rational design of drugs with selective polypharmacology towards guanine nucleotide-binding protein (G-protein)-coupled receptors (GPCRs) to treat central nervous system diseases. Our methodologies rely on the advances in chemocentric informatics and chemogenomics to generate experimentally testable hypotheses that are derived by fusing independent lines of evidence. We posit that such hypothesis fusion approach allows us to improve the overall success rates of in silico lead identification efforts. We have developed an integrated computational approach that combines Quantitative Structure-Activity Relationships (QSAR) modeling, model-based virtual screening (VS), gene expression analysis and mining of the biological literature for drug discovery. The dissertation research described herein is focused on: (1) The development of robust data-driven Quantitative Structure-Activity Relationship (QSAR) models of single target GPCR datasets that will amount to the compendium of GPCR predictors: the GPCR QSARome; (2) The development of robust data-driven QSAR models for families of GPCRs and other trans-membrane molecular targets (i.e., sigma receptors) and the application of models as virtual screening tools for the quick prioritization of compounds for biological testing across receptor families; (3) The development of novel integrative chemocentric informatics approaches to predict receptor-mediated clinical effects of chemicals. Results indicated that our computational efforts to establish a compendium of computational predictors and devise an integrative chemocentric informatics approach to study polypharmacology in silico will eventually lead to useful and reliable tools aimed at identifying and enriching chemical libraries with compounds that have the desired activities for more than one molecular target of interest

Analyzing the Systems Biology Effects of COVID-19 mRNA Vaccines to Assess Their Safety and Putative Side Effects

COVID-19 vaccines have been instrumental tools in reducing the impact of SARS-CoV-2 infections around the world by preventing 80% to 90% of hospitalizations and deaths from reinfection, in addition to preventing 40% to 65% of symptomatic illnesses. However, the simultaneous large-scale vaccination of the global population will indubitably unveil heterogeneity in immune responses as well as in the propensity to developing post-vaccine adverse events, especially in vulnerable individuals. Herein, we applied a systems biology workflow, integrating vaccine transcriptional signatures with chemogenomics, to study the pharmacological effects of mRNA vaccines. First, we derived transcriptional signatures and predicted their biological effects using pathway enrichment and network approaches. Second, we queried the Connectivity Map (CMap) to prioritize adverse events hypotheses. Finally, we accepted higher-confidence hypotheses that have been predicted by independent approaches. Our results reveal that the mRNA-based BNT162b2 vaccine affects immune response pathways related to interferon and cytokine signaling, which should lead to vaccine success, but may also result in some adverse events. Our results emphasize the effects of BNT162b2 on calcium homeostasis, which could be contributing to some frequently encountered adverse events related to mRNA vaccines. Notably, cardiac side effects were signaled in the CMap query results. In summary, our approach has identified mechanisms underlying both the expected protective effects of vaccination as well as possible post-vaccine adverse effects. Our study illustrates the power of systems biology approaches in improving our understanding of the comprehensive biological response to vaccination against COVID-19

Chemocentric Informatics Approach to Drug Discovery: Identification and Experimental Validation of Selective Estrogen Receptor Modulators as Ligands of 5-Hydroxytryptamine-6 Receptors and as Potential Cognition Enhancers

We have devised a chemocentric informatics methodology for drug discovery integrating independent approaches to mining biomolecular databases. As a proof of concept, we have searched for novel putative cognition enhancers. First, we generated Quantitative Structure- Activity Relationship (QSAR) models of compounds binding to 5-hydroxytryptamine-6 receptor (5HT6R), a known target for cognition enhancers, and employed these models for virtual screening to identify putative 5-HT6R actives. Second, we queried chemogenomics data from the Connectivity Map (http://www.broad.mit.edu/cmap/) with the gene expression profile signatures of Alzheimer’s disease patients to identify compounds putatively linked to the disease. Thirteen common hits were tested in 5-HT6R radioligand binding assays and ten were confirmed as actives. Four of them were known selective estrogen receptor modulators that were never reported as 5-HT6R ligands. Furthermore, nine of the confirmed actives were reported elsewhere to have memory-enhancing effects. The approaches discussed herein can be used broadly to identify novel drug-target-disease associations

A cross-sectional study confirms temporary post-COVID-19 vaccine menstrual irregularity and the associated physiological changes among vaccinated women in Jordan

Background: COVID-19 vaccines continue to save people’s lives around the world; however, some vaccine adverse events have been a major concern which slowed down vaccination campaigns. Anecdotal evidence pointed to the vaccine effect on menstruation but evidence from the adverse event reporting systems and the biomedical literature was lacking. This study aimed to investigate the physiological changes in women during menstruation amid the COVID-19 vaccination. Methods: A cross-sectional online survey was distributed to COVID-19 vaccinated women from Nov 2021 to Jan 2022. The results were analyzed using the SPSS software. Results: Among the 564 vaccinated women, 52% experienced significant menstrual irregularities post-vaccination compared to before regardless of the vaccine type. The kind of menstrual irregularity varied among the vaccinated women, for example, 33% had earlier menstruation, while 35% reported delayed menstruation. About 31% experienced heavier menstruation, whereas 24% had lighter menstrual flow. About 29% had menstruation last longer, but 13% had it shorter than usual. Noteworthy, the menstrual irregularities were more frequent after the second vaccine shot, and they disappeared within 3 months on average. Interestingly, 24% of the vaccinated women reported these irregularities to their gynecologist. Conclusion: The COVID-19 vaccine may cause physiological disturbances during menstruation. Luckily, these irregularities were short-termed and should not be a reason for vaccine hesitancy in women. Further studies are encouraged to unravel the COVID-19 vaccine adverse effect on women’s health

Identifying a causal link between prolactin signaling pathways and COVID-19 vaccine-induced menstrual changes

COVID-19 vaccines have been instrumental tools in the fight against SARS-CoV-2 helping to reduce disease severity and mortality. At the same time, just like any other therapeutic, COVID-19 vaccines were associated with adverse events. Women have reported menstrual cycle irregularity after receiving COVID-19 vaccines, and this led to renewed fears concerning COVID-19 vaccines and their effects on fertility. Herein we devised an informatics workflow to explore the causal drivers of menstrual cycle irregularity in response to vaccination with mRNA COVID-19 vaccine BNT162b2. Our methods relied on gene expression analysis in response to vaccination, followed by network biology analysis to derive testable hypotheses regarding the causal links between BNT162b2 and menstrual cycle irregularity. Five high-confidence transcription factors were identified as causal drivers of BNT162b2-induced menstrual irregularity, namely: IRF1, STAT1, RelA (p65 NF-kB subunit), STAT2 and IRF3. Furthermore, some biomarkers of menstrual irregularity, including TNF, IL6R, IL6ST, LIF, BIRC3, FGF2, ARHGDIB, RPS3, RHOU, MIF, were identified as topological genes and predicted as causal drivers of menstrual irregularity. Our network-based mechanism reconstruction results indicated that BNT162b2 exerted biological effects similar to those resulting from prolactin signaling. However, these effects were short-lived and didn’t raise concerns about long-term infertility issues. This approach can be applied to interrogate the functional links between drugs/vaccines and other side effects

New derivatives of sulfonylhydrazone as potential antitumor agents: Design, synthesis and cheminformatics evaluation

Phosphoinositide 3-kinase α (PI3Kα) is a propitious target for designing anticancer drugs. A series of new N\u27-(diphenylmethylene)benzenesulfonohydrazide was synthesized and characterized using FT-IR, NMR (1H and 13C), HRMS, and elemental analysis. Target compounds exhibited an antiproliferative effect against the human colon carcinoma (HCT-116) cell line. Our cheminformatics analysis indicated that the para-tailored derivatives [p-NO2 (3) and p-CF3 (7)] have better ionization potentials based on calculated Moran autocorrelations and ionization potentials. Subsequent in vitro cell proliferation assays validated our cheminformatics results by providing experimental evidence that both derivatives 3 and 7 exhibited improved antiproliferative activities against HCT-116. Hence, our results emphasized the importance of electron-withdrawing groups and hydrogen bond-acceptors in the rational design of small-molecule chemical ligands targeting PI3Kα. These results agreed with the induced-fit docking against PI3Kα, highlighting the role of p-substituted aromatic rings in guiding the ligand-PI3Kα complex formation, by targeting a hydrophobic pocket in the ligand-binding site and forming π-stacking interactions with a nearby tryptophan residue

Development, Validation, and Use of Quantitative Structure−Activity Relationship Models of 5-Hydroxytryptamine (2B) Receptor Ligands to Identify Novel Receptor Binders and Putative Valvulopathic Compounds among Common Drugs

Some antipsychotic drugs are known to cause valvular heart disease by activating serotonin 5-HT2B receptors. We have developed and validated binary classification QSAR models capable of predicting potential 5-HT2B binders. The classification accuracies of the models to discriminate 5-HT2B actives from the inactives were as high as 80% for the external test set. These models were used to screen in silico 59,000 compounds included in the World Drug Index and 122 compounds were predicted as actives with high confidence. Ten of them were tested in radioligand binding assays and nine were found active suggesting a success rate of 90%. All validated binders were then tested in functional assays and one compound was identified as a true 5-HT2B agonist. We suggest that the QSAR models developed in this study could be used as reliable predictors to flag drug candidates that are likely to cause valvulopathy