Multi-dimensional relationships among dementia, depression and prescribed drugs in England and Wales hospitals.

BACKGROUND: Dementia is a group of symptoms that largely affects older people. The majority of patients face behavioural and psychological symptoms (BPSD) during the course of their illness. Alzheimer's disease (AD) and vascular dementia (VaD) are two of the most prevalent types of dementia. Available medications provide symptomatic benefits and provide relief from BPSD and associated health issues. However, it is unclear how specific dementia, antidepressant, antipsychotic, antianxiety, and mood stabiliser drugs, used in the treatment of depression and dementia subtypes are prescribed in hospital admission, during hospital stay, and at the time of discharge. To address this, we apply multi-dimensional data analytical approaches to understand drug prescribing practices within hospitals in England and Wales. METHODS: We made use of the UK National Audit of Dementia (NAD) dataset and pre-processed the dataset. We evaluated the pairwise Pearson correlation of the dataset and selected key data features which are highly correlated with dementia subtypes. After that, we selected drug prescribing behaviours (e.g. specific medications at the time of admission, during the hospital stay, and upon discharge), drugs and disorders. Then to shed light on the relations across multiple features or dimensions, we carried out multiple regression analyses, considering the number of dementia, antidepressant, antipsychotic, antianxiety, mood stabiliser, and antiepileptic/anticonvulsant drug prescriptions as dependent variables, and the prescription of other drugs, number of patients with dementia subtypes (AD/VaD), and depression as independent variables. RESULTS: In terms of antidepressant drugs prescribed in hospital admission, during stay and discharge, the number of sertraline and venlafaxine prescriptions were associated with the number of VaD patients whilst the number of mirtazapine prescriptions was associated with frontotemporal dementia patients. During admission, the number of lamotrigine prescriptions was associated with frontotemporal dementia patients, and with the number of valproate and dosulepin prescriptions. During discharge, the number of mirtazapine prescriptions was associated with the number of donepezil prescriptions in conjunction with frontotemporal dementia patients. Finally, the number of prescriptions of donepezil/memantine at admission, during hospital stay and at discharge exhibited positive association with AD patients. CONCLUSION: Our analyses reveal a complex, multifaceted set of interactions among prescribed drug types, dementia subtypes, and depression.

Shaping a data-driven era in dementia care pathway through computational neurology approaches.

BACKGROUND: Dementia is caused by a variety of neurodegenerative diseases and is associated with a decline in memory and other cognitive abilities, while inflicting an enormous socioeconomic burden. The complexity of dementia and its associated comorbidities presents immense challenges for dementia research and care, particularly in clinical decision-making. MAIN BODY: Despite the lack of disease-modifying therapies, there is an increasing and urgent need to make timely and accurate clinical decisions in dementia diagnosis and prognosis to allow appropriate care and treatment. However, the dementia care pathway is currently suboptimal. We propose that through computational approaches, understanding of dementia aetiology could be improved, and dementia assessments could be more standardised, objective and efficient. In particular, we suggest that these will involve appropriate data infrastructure, the use of data-driven computational neurology approaches and the development of practical clinical decision support systems. We also discuss the technical, structural, economic, political and policy-making challenges that accompany such implementations. CONCLUSION: The data-driven era for dementia research has arrived with the potential to transform the healthcare system, creating a more efficient, transparent and personalised service for dementia.

Degeneracy and stability in neural circuits of dopamine and serotonin neuromodulators: A theoretical consideration.

Degenerate neural circuits perform the same function despite being structurally different. However, it is unclear whether neural circuits with interacting neuromodulator sources can themselves degenerate while maintaining the same neuromodulatory function. Here, we address this by computationally modeling the neural circuits of neuromodulators serotonin and dopamine, local glutamatergic and GABAergic interneurons, and their possible interactions, under reward/punishment-based conditioning tasks. The neural modeling is constrained by relevant experimental studies of the VTA or DRN system using, e.g., electrophysiology, optogenetics, and voltammetry. We first show that a single parsimonious, sparsely connected neural circuit model can recapitulate several separate experimental findings that indicated diverse, heterogeneous, distributed, and mixed DRNVTA neuronal signaling in reward and punishment tasks. The inability of this model to recapitulate all observed neuronal signaling suggests potentially multiple circuits acting in parallel. Then using computational simulations and dynamical systems analysis, we demonstrate that several different stable circuit architectures can produce the same observed network activity profile, hence demonstrating degeneracy. Due to the extensive D2-mediated connections in the investigated circuits, we simulate the D2 receptor agonist by increasing the connection strengths emanating from the VTA DA neurons. We found that the simulated D2 agonist can distinguish among sub-groups of the degenerate neural circuits based on substantial deviations in specific neural populations' activities in reward and punishment conditions. This forms a testable model prediction using pharmacological means. Overall, this theoretical work suggests the plausibility of degeneracy within neuromodulator circuitry and has important implications for the stable and robust maintenance of neuromodulatory functions.

Distinguishing Lewy Body Dementia from Alzheimer's Disease using Machine Learning on Heterogeneous Data: A Feasibility Study.

Dementia with Lewy Bodies (DLB) is the second most common form of dementia, but diagnostic markers for DLB can be expensive and inaccessible, and many cases of DLB are undiagnosed. This work applies machine learning techniques to determine the feasibility of distinguishing DLB from Alzheimer's Disease (AD) using heterogeneous data features. The Repeated Incremental Pruning to Produce Error Reduction (RIPPER) algorithm was first applied using a Leave-One-Out Cross-Validation protocol to a dataset comprising DLB and AD cases. Then, interpretable association rule-based diagnostic classifiers were obtained for distinguishing DLB from AD. The various diagnostic classifiers generated by this process had high accuracy over the whole dataset (mean accuracy of 94%). The mean accuracy in classifying their out-of-sample case was 80.5%. Every classifier generated consisted of very simple structure, each using 1-2 classification rules and 1-3 data features. As a group, the classifiers were heterogeneous and used several different data features. In particular, some of the classifiers used very simple and inexpensive diagnostic features, yet with high diagnostic accuracy. This work suggests that opportunities may exist for incorporating accessible diagnostic assessments while improving diagnostic rate for DLB. Clinical Relevance- Simple and interpretable high-performing machine learning algorithms identified a variety of readily available clinical assessments for differential diagnosis of dementia offering the opportunities to incorporate various simple and inexpensive screening tests for DLB and addressing the problem of DLB underdiagnosis.

Secondary infections modify the overall course of hospitalized patients with COVID-19: a retrospective study from a network of hospitals across North India.

Objective: To gain better insight into the extent of secondary bacterial and fungal infections in hospitalized patients in India, and to assess how these alter the course of coronavirus disease 2019 (COVID-19) so that control measures can be suggested. Methods: In this retrospective, multicentre study, the data of all patients who tested positive for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) on reverse transcriptase polymerase chain reaction (RT-PCR), admitted to hospital between March 2020 and July 2021, were accessed from the electronic health records of a network of 10 hospitals across five states in North India. Results: Of 19,852 patients testing positive for SARS-CoV-2 on RT-PCR and admitted to the study hospitals during the study period, 1940 (9.8%) patients developed secondary infections (SIs). Patients with SIs were, on average, 8 years older than patients without SIs (median age 62.6 vs 54.3 years; P<0.001). The risk of SIs was significantly (P<0.001) associated with age, severity of disease at admission, diabetes, admission to the intensive care unit (ICU), and ventilator use. The most common site of infection was urine (41.7%), followed by blood (30.8%) and sputum/bronchoalveolar lavage/endotracheal fluid (24.8%); the least common was pus/wound discharge (2.6%). Gram-negative bacilli (GNB) were the most common organisms (63.2%), followed by Gram-positive cocci (GPC) (19.6%) and fungi (17.3%). Most patients with SIs were on multiple antimicrobials. The most commonly used antibiotics against GNB were beta-lactam/beta-lactamase inhibitors (76.9%), carbapenems (57.7%), cephalosporins (53.9%), and antibiotics against carbapenem-resistant Enterobacteriaceae (47.1%). Empirical use of antibiotics against GPC was seen in 58.9% of patients with SIs, and empirical use of antifungals was observed in 56.9% of patients with SIs. The average length of hospital stay for patients with SIs was almost twice as long as that of patients without SIs (median 13 vs 7 days). Overall mortality among patients with SIs (40.3%) was more than eight times higher than that among patients without SIs (4.6%). Only 1.2% of patients with SIs with mild COVID-19 at admission died, compared with 17.5% of those with moderate COVID-19 at admission and 58.5% of those with severe COVID-19 at admission (P<0.001). The mortality rate was highest in patients with bloodstream infections (49.8%), followed by those with hospital-acquired pneumonia (47.9%), urinary tract infections (29.4%), and skin and soft tissue infections (29.4%). The mortality rate in patients with diabetes with SIs was 45.2%, compared with 34.3% in those without diabetes (P<0.001). Conclusions: SIs complicate the course of patients hospitalized with COVID-19. These patients tend to have a much longer hospital stay, a higher requirement for oxygen and ICU care, and a significantly higher mortality rate compared with those without SIs. The groups most vulnerable to SIs are patients with more severe COVID-19, elderly patients and patients with diabetes. Judicious empirical use of combination antimicrobials in these groups of vulnerable patients can save lives. It is desirable to have region- or country-specific guidelines for appropriate use of antibiotics and antifungals to prevent their overuse.

An Early Stage Researcher's Primer on Systems Medicine Terminology.

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic.

Opportunities for multiscale computational modelling of serotonergic drug effects in Alzheimer's disease.

Alzheimer's disease (AD) is an age-specific neurodegenerative disease that compromises cognitive functioning and impacts the quality of life of an individual. Pathologically, AD is characterised by abnormal accumulation of beta-amyloid (Aß) and hyperphosphorylated tau protein. Despite research advances over the last few decades, there is currently still no cure for AD. Although, medications are available to control some behavioural symptoms and slow the disease's progression, most prescribed medications are based on cholinesterase inhibitors. Over the last decade, there has been increased attention towards novel drugs, targeting alternative neurotransmitter pathways, particularly those targeting serotonergic (5-HT) system. In this review, we focused on 5-HT receptor (5-HTR) mediated signalling and drugs that target these receptors. These pathways regulate key proteins and kinases such as GSK-3 that are associated with abnormal levels of Aß and tau in AD. We then review computational studies related to 5-HT signalling pathways with the potential for providing deeper understanding of AD pathologies. In particular, we suggest that multiscale and multilevel modelling approaches could potentially provide new insights into AD mechanisms, and towards discovering novel 5-HTR based therapeutic targets.

An integrated modelling framework for neural circuits with multiple neuromodulators.

Neuromodulators are endogenous neurochemicals that regulate biophysical and biochemical processes, which control brain function and behaviour, and are often the targets of neuropharmacological drugs. Neuromodulator effects are generally complex partly owing to the involvement of broad innervation, co-release of neuromodulators, complex intra- and extrasynaptic mechanism, existence of multiple receptor subtypes and high interconnectivity within the brain. In this work, we propose an efficient yet sufficiently realistic computational neural modelling framework to study some of these complex behaviours. Specifically, we propose a novel dynamical neural circuit model that integrates the effective neuromodulator-induced currents based on various experimental data (e.g. electrophysiology, neuropharmacology and voltammetry). The model can incorporate multiple interacting brain regions, including neuromodulator sources, simulate efficiently and easily extendable to large-scale brain models, e.g. for neuroimaging purposes. As an example, we model a network of mutually interacting neural populations in the lateral hypothalamus, dorsal raphe nucleus and locus coeruleus, which are major sources of neuromodulator orexin/hypocretin, serotonin and norepinephrine/noradrenaline, respectively, and which play significant roles in regulating many physiological functions. We demonstrate that such a model can provide predictions of systemic drug effects of the popular antidepressants (e.g. reuptake inhibitors), neuromodulator antagonists or their combinations. Finally, we developed user-friendly graphical user interface software for model simulation and visualization for both fundamental sciences and pharmacological studies.

Immunogenicity and protective efficacy of inactivated equine influenza (H3N8) virus vaccine in murine model.

Equine influenza viruses (EIVs) are responsible for acute contagious respiratory infection in equines and the disease remains a major threat for equine population throughout the world despite vaccination strategies in place. The present study was aimed to assess the suitability of BALB/c mice as a potential small animal model for preliminary screening of EI vaccine candidates. For this, we evaluated the immunogenicity and protective efficacy of an inactivated EIV (H3N8) vaccine in BALB/c mouse model after challenge with homologous H3N8 virus (Clade 2 virus, Florida sublineage) through serology, clinical signs, gross and histopathology lesions with grading, immunohistochemistry and virus quantification. Serological responses in immunized mice were evaluated by haemagglutination inhibition assay (HAI) and antibodies were subtyped by ELISA. The vaccine induced optimum protective antibody titre on 49 dpi along with balanced Th1/Th2 responses. Immunized mice were well protected against EIV challenge as evident by significant rise in serum antibody titre which concurred with mild clinical signs, early recovery, lower gross and histopathological lesions score, less severe intensity of viral antigen distribution, restricted virus replication in respiratory tract and less virus detection in nasal washes for short duration. The duration of the viral load was also lower and only for brief period as compared to unvaccinated challenged mice. In conclusion, induction of H3N8 specific antibody response and protection against H3N8 challenge proves that egg grown inactivated H3N8 whole virus vaccine would provide an effective intercession against H3N8 virus. In addition, BALB/c mouse can serve as an attractive tool for adjudging protective efficacy of vaccine candidates prior to final testing in equines.

Divergence and inheritance of neocortical heterotopia in inbred and genetically-engineered mice.

Cortical function emerges from the intrinsic properties of neocortical neurons and their synaptic connections within and across lamina. Neurodevelopmental disorders affecting migration and lamination of the neocortex result in cognitive delay/disability and epilepsy. Molecular layer heterotopia (MLH), a dysplasia characterized by over-migration of neurons into layer I, are associated with cognitive deficits and neuronal hyperexcitability in humans and mice. The breadth of different inbred mouse strains that exhibit MLH and inheritance patterns of heterotopia remain unknown. A neuroanatomical survey of numerous different inbred mouse strains, 2 first filial generation (F1) hybrids, and one consomic strain (C57BL/6J-Chr 1A/J/NaJ) revealed MLH only in C57BL/6 mice and the consomic strain. Heterotopia were observed in numerous genetically-engineered mouse lines on a congenic C57BL/6 background. These data indicate that heterotopia formation is a weakly penetrant trait requiring homozygosity of one or more C57BL/6 alleles outside of chromosome 1. These data are relevant toward understanding neocortical development and disorders affecting neocortical lamination.

Novel, Precise, Accurate Ion-Pairing Method to Determine the Related Substances of the Fondaparinux Sodium Drug Substance: Low-Molecular-Weight Heparin.

Fondaparinux sodium is a synthetic low-molecular-weight heparin (LMWH). This medication is an anticoagulant or a blood thinner, prescribed for the treatment of pulmonary embolism and prevention and treatment of deep vein thrombosis. Its determination in the presence of related impurities was studied and validated by a novel ion-pair HPLC method. The separation of the drug and its degradation products was achieved with the polymer-based PLRPs column (250 mm × 4.6 mm; 5 µm) in gradient elution mode. The mixture of 100 mM n-hexylamine and 100 mM acetic acid in water was used as buffer solution. Mobile phase A and mobile phase B were prepared by mixing the buffer and acetonitrile in the ratio of 90:10 (v/v) and 20:80 (v/v), respectively. Mobile phases were delivered in isocratic mode (2% B for 0-5 min) followed by gradient mode (2-85% B in 5-60 min). An Evaporative Light Scattering Detector (ELSD) was connected to the LC system to detect the responses of chromatographic separation. Further, the drug was subjected to stress studies for acidic, basic, oxidative, photolytic, and thermal degradations as per ICH guidelines and the drug was found to be labile in acid, base hydrolysis, and oxidation, while stable in neutral, thermal, and photolytic degradation conditions. The method provided linear responses over the concentration range of the LOQ to 0.30% for each impurity with respect to the analyte concentration of 12.5 mg/mL, and regression analysis showed a correlation coefficient value (r(2)) of more than 0.99 for all the impurities. The LOD and LOQ were found to be 1.4 µg/mL and 4.1 µg/mL, respectively, for fondaparinux. The developed ion-pair method was validated as per ICH guidelines with respect to accuracy, selectivity, precision, linearity, and robustness.

Pathology of Equine Influenza virus (H3N8) in Murine Model.

Equine influenza viruses (EIV)-H3N8 continue to circulate in equine population throughout the world. They evolve by the process of antigenic drift that leads to substantial change in the antigenicity of the virus, thereby necessitating substitution of virus strain in the vaccines. This requires frequent testing of the new vaccines in the in vivo system; however, lack of an appropriate laboratory animal challenge model for testing protective efficacy of equine influenza vaccine candidates hinders the screening of new vaccines and other therapeutic approaches. In the present investigation, BALB/c mouse were explored for suitability for conducting pathogenecity studies for EIV. The BALB/c mice were inoculated intranasally @ 2×106.24 EID50 with EIV (H3N8) belonging to Clade 2 of Florida sublineage and monitored for setting up of infection and associated parameters. All mice inoculated with EIV exhibited clinical signs viz. loss in body weights, lethargy, dyspnea, etc, between 3 and 5 days which commensurate with lesions observed in the respiratory tract including rhinitis, tracheitis, bronchitis, bronchiolitis, alveolitis and diffuse interstitial pneumonia. Transmission electron microscopy, immunohistochemistry, virus quantification through titration and qRT-PCR demonstrated active viral infection in the upper and lower respiratory tract. Serology revealed rise in serum lactate dehydrogenase levels along with sero-conversion. The pattern of disease progression, pathological lesions and virus recovery from nasal washings and lungs in the present investigations in mice were comparable to natural and experimental EIV infection in equines. The findings establish BALB/c mice as small animal model for studying EIV (H3N8) infection and will have immense potential for dissecting viral pathogenesis, vaccine efficacy studies, preliminary screening of vaccine candidates and antiviral therapeutics against EIV.

Neural circuit interactions between the dorsal raphe nucleus and the lateral hypothalamus: an experimental and computational study.

Orexinergic/hypocretinergic (Ox) neurotransmission plays an important role in regulating sleep, as well as in anxiety and depression, for which the serotonergic (5-HT) system is also involved in. However, little is known regarding the direct and indirect interactions between 5-HT in the dorsal raphe nucleus (DRN) and Ox neurons in the lateral hypothalamus (LHA). In this study, we report the additional presence of 5-HT1BR, 5-HT2AR, 5-HT2CR and fast ligand-gated 5-HT3AR subtypes on the Ox neurons of transgenic Ox-enhanced green fluorescent protein (Ox-EGFP) and wild type C57Bl/6 mice using single and double immunofluorescence (IF) staining, respectively, and quantify the colocalization for each 5-HT receptor subtype. We further reveal the presence of 5-HT3AR and 5-HT1AR on GABAergic neurons in LHA. We also identify NMDAR1, OX1R and OX2R on Ox neurons, but none on adjacent GABAergic neurons. This suggests a one-way relationship between LHA's GABAergic and Ox neurons, wherein GABAergic neurons exerts an inhibitory effect on Ox neurons under partial DRN's 5-HT control. We also show that Ox axonal projections receive glutamatergic (PSD-95 immunopositive) and GABAergic (Gephyrin immunopositive) inputs in the DRN. We consider these and other available findings into our computational model to explore possible effects of neural circuit connection types and timescales on the DRN-LHA system's dynamics. We find that if the connections from 5-HT to LHA's GABAergic neurons are weakly excitatory or inhibitory, the network exhibits slow oscillations; not observed when the connection is strongly excitatory. Furthermore, if Ox directly excites 5-HT neurons at a fast timescale, phasic Ox activation can lead to an increase in 5-HT activity; no significant effect with slower timescale. Overall, our experimental and computational approaches provide insights towards a more complete understanding of the complex relationship between 5-HT in the DRN and Ox in the LHA.

Network properties of a computational model of the dorsal raphe nucleus.

Serotonin (5-HT) plays an important role in regulating mood, cognition and behaviour. The midbrain dorsal raphe nucleus (DRN) is one of the primary sources of 5-HT. Recent studies show that DRN neuronal activities can encode rewarding (e.g., appetitive) and unrewarding (e.g., aversive) behaviours. Experiments have also shown that DRN neurons can exhibit heterogeneous spiking behaviours. In this work, we build and study a basic spiking neuronal network model of the DRN constrained by neuronal properties observed in experiments. We use an efficient adaptive quadratic integrate-and-fire neuronal model to capture slow afterhyperpolarization current, occasional bursting behaviours in 5-HT neurons, and fast spiking activities in the non-5-HT inhibitory neurons. Provided that our noisy and heterogeneous spiking neuronal network model adopts a feedforward inhibitory network architecture, it is able to replicate the main features of DRN neuronal activities recorded in monkeys performing a reward-based memory-guided saccade task. The model exhibits theta band oscillation, especially among the non-5-HT inhibitory neurons during the rewarding outcome of a simulated trial, thus forming a model prediction. By varying the inhibitory synaptic strengths and the afferent inputs, we find that the network model can oscillate over a range of relatively low frequencies, allow co-existence of multiple stable frequencies, and spike synchrony can spread from within a local neural subgroup to global. Our model suggests plausible network architecture, provides interesting model predictions that can be experimentally tested, and offers a sufficiently realistic multi-scale model for 5-HT neuromodulation simulations.

A study of prevalence of depression in diabetes mellitus: analysis from urban India.

BACKGROUND: The chronic debilitating conditions, i.e., diabetes and depression are associated with significant morbidity, mortality, and healthcare costs. Both these interlinked chronic conditions contribute to their worst outcomes. AIMS: The objective of the present study was to analyze the frequency of depression in diabetes and its correlation with demographic details like age, sex, domicile, education, income, and marital status. SETTINGS AND DESIGN: The study was conducted in the private diabetic clinic in outer Delhi (Rohini). SUBJECTS AND METHODS: Totally, 250 patients attending the outpatient department of private diabetic clinic were assessed with Beck Depression Inventory Scale which was a 21-question multiple-choice self-report inventory. RESULTS: Among the study population, 11.6% of the patients had co-morbid depression with more prevalence in females when compared with males. The demographic analysis revealed that depression incidence was higher in urban population, lower socio-economic class, and in patients with diabetes more than 5 years when compared with their counterparts and similar in graduate and undergraduate student population. Interestingly, the numbers of depression cases were found only in married population contrary to none in unmarried category. CONCLUSION: Present study concluded that co-morbidity of depression is prevalent in diabetic population with three times higher frequency than the considered feature of mild depression.

A mathematical model to explore the interdependence between the serotonin and orexin/hypocretin systems.

Among their multitude of physiological and behavioral effects, the neurochemicals serotonin (5-HT) and orexin (Ox) have been closely linked to major depressive disorders (MDD) and sleep alterations. The dorsal raphe nucleus (DRN) and the lateral hypothalamus area (LHA) are brain regions that are sources of 5-HT and Ox, and there is evidence that suggests a reciprocal interaction between them. This lends support to the hypothesis of a close relationship between MDD and sleep disorders. Based on various experimental data, and appropriate assumptions, we construct a mathematical model of the coupled DRN-LHA neural circuit. Our model relates the dynamics of four important variables that can be experimentally measured: (i) the firing rate of 5-HT-containing neurons in DRN, (ii) the firing rate of Ox-containing neurons in the LHA, (iii) 5-HT concentration level in LHA, and (iv) Ox concentration level in DRN. Simulations show that our model supports the co-existence of baseline activities and concentration levels as observed in various separate experiments. It also allows circuit-level exploration of various parameters not yet identified experimentally, e.g. the rise and decay of Ox concentration levels due to Ox neural activity, and the exact dependence of Ox neural activity on 5-HT level. Finally we have made some model predictions regarding the effects of the 5-HT antagonist on the circuit. Our model, which can be subjected to verification and refinement as new experimental data accumulates, provides unified quantitative relationships and predictions between two important connected brain regions strongly tied to MDD and sleep disorders.