Protein by-products: Composition, extraction, and biomedical applications.

Significant upsurge in animal by-products such as skin, bones, wool, hides, feathers, and fats has become a global challenge and, if not properly disposed of, can spread contamination and viral diseases. Animal by-products are rich in proteins, which can be used as nutritional, pharmacologically functional ingredients, and biomedical materials. Therefore, recycling these abundant and renewable by-products and extracting high value-added components from them is a sustainable approach to reclaim animal by-products while addressing scarce landfill resources. This article appraises the most recent studies conducted in the last five years on animal-derived proteins' separation and biomedical application. The effort encompasses an introduction about the composition, an overview of the extraction and purification methods, and the broad range of biomedical applications of these ensuing proteins.

Sleep movements and respiratory coupling as a biobehavioral metric for early Alzheimer's disease in independently dwelling adults.

INTRODUCTION: Sleep disorder is often the first symptom of age-related cognitive decline associated with Alzheimer's disease (AD) observed in primary care. The relationship between sleep and early AD was examined using a patented sleep mattress designed to record respiration and high frequency movement arousals. A machine learning algorithm was developed to classify sleep features associated with early AD. METHOD: Community-dwelling older adults (N = 95; 62-90 years) were recruited in a 3-h catchment area. Study participants were tested on the mattress device in the home bed for 2 days, wore a wrist actigraph for 7 days, and provided sleep diary and sleep disorder self-reports during the 1-week study period. Neurocognitive testing was completed in the home within 30-days of the sleep study. Participant performance on executive and memory tasks, health history and demographics were reviewed by a geriatric clinical team yielding Normal Cognition (n = 45) and amnestic MCI-Consensus (n = 33) groups. A diagnosed MCI group (n = 17) was recruited from a hospital memory clinic following diagnostic series of neuroimaging biomarker assessment and cognitive criteria for AD. RESULTS: In cohort analyses, sleep fragmentation and wake after sleep onset duration predicted poorer executive function, particularly memory performance. Group analyses showed increased sleep fragmentation and total sleep time in the diagnosed MCI group compared to the Normal Cognition group. Machine learning algorithm showed that the time latency between movement arousals and coupled respiratory upregulation could be used as a classifier of diagnosed MCI vs. Normal Cognition cases. ROC diagnostics identified MCI with 87% sensitivity; 89% specificity; and 88% positive predictive value. DISCUSSION: AD sleep phenotype was detected with a novel sleep biometric, time latency, associated with the tight gap between sleep movements and respiratory coupling, which is proposed as a corollary of sleep quality/loss that affects the autonomic regulation of respiration during sleep. Diagnosed MCI was associated with sleep fragmentation and arousal intrusion.

Influence of Occlusal Hypofunction on Alveolar Bone Healing in Rats.

The aim of this in vivo study was to investigate the effect of occlusal hypofunction on alveolar bone healing in the absence or presence of an enamel matrix derivative (EMD). A standardized fenestration defect over the root of the mandibular first molar in 15 Wistar rats was created. Occlusal hypofunction was induced by extraction of the antagonist. Regenerative therapy was performed by applying EMD to the fenestration defect. The following three groups were established: (a) normal occlusion without EMD treatment, (b) occlusal hypofunction without EMD treatment, and (c) occlusal hypofunction with EMD treatment. After four weeks, all animals were sacrificed, and histological (hematoxylin and eosin, tartrate-resistant acid phosphatase) as well as immunohistochemical analyses (periostin, osteopontin, osteocalcin) were performed. The occlusal hypofunction group showed delayed bone regeneration compared to the group with normal occlusion. The application of EMD could partially, but not completely, compensate for the inhibitory effects of occlusal hypofunction on bone healing, as evidenced by hematoxylin and eosin and immunohistochemistry for the aforementioned molecules. Our results suggest that normal occlusal loading, but not occlusal hypofunction, is beneficial to alveolar bone healing. Adequate occlusal loading appears to be as advantageous for alveolar bone healing as the regenerative potential of EMD.

Postpartum depression and its clinico-social correlates - A community-based study in aligarh.

Context: Postpartum depression (PPD) is onset of depressive symptoms in postpartum period from 2 weeks to 1 year. It causes maternal morbidity and long-term negative effects on growth and development of infant and child. It is often unreported and underdiagnosed. Aims: (1) To estimate the prevalence of PPD, (2) To determine socio-demographic, clinical, and obstetric correlates of the same. Settings and Design: A cross-sectional study was done in urban and rural areas of District Aligarh. Methods: A total of 304 females between 6 weeks and 6 months' postpartum period giving consent were included in this study. Sociodemographic, obstetric, and clinico-social factors were recorded using predesigned, pretested questionnaire. Edinburgh Postnatal Depression Scale (EPDS) score ≥10 was used to screen for PPD and International Classification of Disease (ICD-10) criteria for confirmation. Statistical Analysis Used: Correlates of PPD were determined using logistic regression analysis. Results: The prevalence of PPD was 9.5% using EPDS and was confirmed by ICD-10 criteria. History of abortion (adjusted odds ratio [AOR]: 6.0, 95% Confidence Interval [CI] 2.2-16.5), poor relationship with in-laws (AOR: 5.1; 95% CI 1.3-20.5), marital conflict (AOR: 13.3; 95% CI 2.2-77.6), and substance abuse in husband (AOR: 3.1; 95% CI 1.1-9.0) were found to be significant correlates for PPD. Conclusions: About one in every 10 postpartum females suffered from depression but did not seek health care for the same. Women facing social pathologies such as substance abuse in husband, marital conflict, and poor relationship with in-laws are more at risk of PPD. Screening for PPD should be included in the maternal and child health care programs to ensure early diagnosis and treatment.

Effects of Obesity on Bone Healing in Rats.

Although the association between periodontitis and obesity is well explored, it is unclear whether obesity is associated with a worse therapeutic outcome after periodontal treatment. The aim of this study was to investigate the effects of obesity on bone healing with and without the application of regeneration-promoting molecules. A standardized bone fenestration-type defect was created over the root of the mandibular first molar in 15 Wistar rats. Ten animals received a high-fat, high-sucrose diet (HFSD), while the remaining five animals were fed a standard diet. During surgery, the fenestration defects from half of the HFSD-fed, i.e., obese animals, were treated with regeneration-promoting molecules (enamel matrix derivative; EMD). After four weeks, bone healing was evaluated by histomorphometry, TRAP staining and immunohistochemistry for RUNX2 and osteopontin. The analyses revealed that the spontaneous healing of the periodontal defects was compromised by obesity. Application of EMD partially compensated for the negative effect of obesity. Nevertheless, EMD-stimulated bone healing in obese animals was not better than the spontaneous healing in the obesity-free control group, indicating that obesity may also inhibit the stimulatory effects of regeneration-promoting molecules. Our results show that obesity can negatively influence bone healing and suggest that bone healing may be compromised in humans.

Comparing Serum Levels of Vitamin D and Zinc in Novel Coronavirus-Infected Patients and Healthy Individuals in Northeastern Iran, 2020.

BACKGROUND: COVID-19 infection has recently become a pandemic disease around the world, and its risk factors have not fully evaluated. This study aimed to compare the serum vitamin D (Vit D) and zinc levels in patients infected with novel coronavirus and healthy volunteers (HVs). METHODS: This was a single-center, cross-sectional study conducted on 56 patients (32 severe cases and 24 nonsevere) admitted to the COVID-19 ward and 46 HVs living in Esfarayen City, North Khorasan Province of Iran. Serum levels of Vit D and zinc in admitted patients to the COVID-19 ward and HVs were measured. RESULTS: The average levels of serum Vit D in severe cases, nonsevere cases, and HVs were 31.03 ± 15.49, 37.25 ± 18.49, and 39.33 ± 14.83, respectively (P = 0.05). Moreover, the average concentrations of serum zinc in severe cases, nonsevere cases, and HVs were 31.03 ± 15.49, 37.25 ± 18.49, and 39.33 ± 14.83, respectively (P = 0.01). Mortality rate, reinfection (for 5 months), and length of hospital stay in severe cases were higher than in nonsevere cases (P > 0.05). CONCLUSIONS: Results showed that severe cases had lower levels of Vit D than did other groups and were marginally significant. Also, severe cases had a significantly low level of zinc when compared with nonsevere cases and HVs. Levels of Vit D and zinc can affect the incidence of COVID-19 infection.

Electronic non-adiabatic dynamics in enhanced ionization of isotopologues of hydrogen molecular ions from the exact factorization perspective.

It was recently shown that the exact potential driving the electron's dynamics in enhanced ionization of H2+ can have large contributions arising from dynamic electron-nuclear correlation, going beyond what any Coulombic-based model can provide. This potential is defined via the exact factorization of the molecular wavefunction that allows the construction of a Schrödinger equation for the electronic system, in which the potential contains exactly the effect of coupling to the nuclear system and any external fields. Here we study enhanced ionization in isotopologues of H2+ in order to investigate the nuclear-mass-dependence of these terms for this process. We decompose the exact potential into components that naturally arise from the conditional wavefunction, and also into components arising from the marginal electronic wavefunction, and compare the performance of propagation on these different components as well as approximate potentials based on the quasi-static or Hartree approximation with the exact propagation. A quasiclassical analysis is presented to help analyse the structure of different non-Coulombic components of the potential driving the ionizing electron.

Exact Potential Driving the Electron Dynamics in Enhanced Ionization of H(2)(+).

It was recently shown that the exact factorization of the electron-nuclear wave function allows the construction of a Schrödinger equation for the electronic system, in which the potential contains exactly the effect of coupling to the nuclear degrees of freedom and any external fields. Here we study the exact potential acting on the electron in charge-resonance enhanced ionization in a model one-dimensional H(2)(+) molecule. We show there can be significant differences between the exact potential and that used in the traditional quasistatic analyses, arising from nonadiabatic coupling to the nuclear system, and that these are crucial to include for accurate simulations of time-resolved ionization dynamics and predictions of the ionization yield.

Laser-induced electron localization in H2⁺: mixed quantum-classical dynamics based on the exact time-dependent potential energy surface.

We study the exact nuclear time-dependent potential energy surface (TDPES) for laser-induced electron localization with a view to eventually developing a mixed quantum-classical dynamics method for strong-field processes. The TDPES is defined within the framework of the exact factorization [A. Abedi, N. T. Maitra, and E. K. U. Gross, Phys. Rev. Lett., 2010, 105, 123002] and contains the exact effect of the couplings to the electronic subsystem and to any external fields within a scalar potential. We compare its features with those of the quasistatic potential energy surfaces (QSPES) often used to analyse strong-field processes. We show that the gauge-independent component of the TDPES has a mean-field-like character very close to the density-weighted average of the QSPESs. Oscillations in this component are smoothened out by the gauge-dependent component, and both components are needed to yield the correct force on the nuclei. Once the localization begins to set in, the gradient of the exact TDPES tracks one QSPES and then switches to the other, similar to the description provided by surface-hopping between QSPESs. We show that evolving an ensemble of classical nuclear trajectories on the exact TDPES accurately reproduces the exact dynamics. This study suggests that the mixed quantum-classical dynamics scheme based on evolving multiple classical nuclear trajectories on the exact TDPES will be a novel and useful method to simulate strong field processes.

The exact forces on classical nuclei in non-adiabatic charge transfer.

The decomposition of electronic and nuclear motion presented in Abedi et al. [Phys. Rev. Lett. 105, 123002 (2010)] yields a time-dependent potential that drives the nuclear motion and fully accounts for the coupling to the electronic subsystem. Here, we show that propagation of an ensemble of independent classical nuclear trajectories on this exact potential yields dynamics that are essentially indistinguishable from the exact quantum dynamics for a model non-adiabatic charge transfer problem. We point out the importance of step and bump features in the exact potential that are critical in obtaining the correct splitting of the quasiclassical nuclear wave packet in space after it passes through an avoided crossing between two Born-Oppenheimer surfaces and analyze their structure. Finally, an analysis of the exact potentials in the context of trajectory surface hopping is presented, including preliminary investigations of velocity-adjustment and the force-induced decoherence effect.

Correlated electron-nuclear dynamics with conditional wave functions.

The molecular Schrödinger equation is rewritten in terms of nonunitary equations of motion for the nuclei (or electrons) that depend parametrically on the configuration of an ensemble of generally defined electronic (or nuclear) trajectories. This scheme is exact and does not rely on the tracing out of degrees of freedom. Hence, the use of trajectory-based statistical techniques can be exploited to circumvent the calculation of the computationally demanding Born-Oppenheimer potential-energy surfaces and nonadiabatic coupling elements. The concept of the potential-energy surface is restored by establishing a formal connection with the exact factorization of the full wave function. This connection is used to gain insight from a simplified form of the exact propagation scheme.

Is the molecular Berry phase an artifact of the Born-Oppenheimer approximation?

We demonstrate that the molecular Berry phase and the corresponding nonanalyticity in the electronic Born-Oppenheimer wave function is, in general, not a true topological feature of the exact solution of the full electron-nuclear Schrödinger equation. For a numerically exactly solvable model we show that a nonanalyticity, and the associated geometric phase, only appear in the limit of infinite nuclear mass, while a perfectly smooth behavior is found for any finite nuclear mass.

Classical nuclear motion coupled to electronic non-adiabatic transitions.

Based on the exact factorization of the electron-nuclear wave function, we have recently proposed a mixed quantum-classical scheme [A. Abedi, F. Agostini, and E. K. U. Gross, Europhys. Lett. 106, 33001 (2014)] to deal with non-adiabatic processes. Here we present a comprehensive description of the formalism, including the full derivation of the equations of motion. Numerical results are presented for a model system for non-adiabatic charge transfer in order to test the performance of the method and to validate the underlying approximations.

Effect of geometrical design variables on implantation configuration and fixation stiffness of titling bone anchors: A parametric finite element study.

The mechanical interaction of a tilting anchor and cancellous bones of various densities was simulated using finite element modeling. The model enjoyed a sophisticated representation of the bone, as an elasto-plastic material with large deformation capability. The anchor's tilting action during implantation phase, as well as its fixation stiffness during pull-out test, were predicted by the model and a parametric study was performed to investigate the effects of the anchor's distal width and corner fillet radius, on these measures. The model predictions were validated against the results of an experimental test on ovine humerus specimens. The model could reasonably reproduce the tilting action of the anchor during the implantation phase. Comparison of the model predictions with the experimental results revealed similar trends during both the implantation and the pull-out phases, but smaller displacement magnitudes (end points: 1.4 vs. 2.1 mm and 4.6 vs. 5.2 mm, respectively). The results of the parametric study indicated substantial increase in the fixation stiffness with increasing bone density. Reducing the distal width and increasing the fillet radius improved the anchor's implantation configuration and fixation stiffness in low-density bones. For high-density bone applications, however, a larger distal width was favored for improving the fixation stiffness.

Artificial intelligence-driven virtual rehabilitation for people living in the community: A scoping review.

Virtual Rehabilitation (VRehab) is a promising approach to improving the physical and mental functioning of patients living in the community. The use of VRehab technology results in the generation of multi-modal datasets collected through various devices. This presents opportunities for the development of Artificial Intelligence (AI) techniques in VRehab, namely the measurement, detection, and prediction of various patients' health outcomes. The objective of this scoping review was to explore the applications and effectiveness of incorporating AI into home-based VRehab programs. PubMed/MEDLINE, Embase, IEEE Xplore, Web of Science databases, and Google Scholar were searched from inception until June 2023 for studies that applied AI for the delivery of VRehab programs to the homes of adult patients. After screening 2172 unique titles and abstracts and 51 full-text studies, 13 studies were included in the review. A variety of AI algorithms were applied to analyze data collected from various sensors and make inferences about patients' health outcomes, most involving evaluating patients' exercise quality and providing feedback to patients. The AI algorithms used in the studies were mostly fuzzy rule-based methods, template matching, and deep neural networks. Despite the growing body of literature on the use of AI in VRehab, very few studies have examined its use in patients' homes. Current research suggests that integrating AI with home-based VRehab can lead to improved rehabilitation outcomes for patients. However, further research is required to fully assess the effectiveness of various forms of AI-driven home-based VRehab, taking into account its unique challenges and using standardized metrics.

On the evaluating membrane flux of forward osmosis systems: Data assessment and advanced intelligent modeling.

As an emerging desalination technology, forward osmosis (FO) can potentially become a reliable method to help remedy the current water crisis. Introducing uncomplicated and precise models could help FO systems' optimization. This paper presents the prediction and evaluation of FO systems' membrane flux using various artificial intelligence-based models. Detailed data gathering and cleaning were emphasized because appropriate modeling requires precise inputs. Accumulating data from the original sources, followed by duplicate removal, outlier detection, and feature selection, paved the way to begin modeling. Six models were executed for the prediction task, among which two are tree-based models, two are deep learning models, and two are miscellaneous models. The calculated coefficient of determination (R2 ) of our best model (XGBoost) was 0.992. In conclusion, tree-based models (XGBoost and CatBoost) show more accurate performance than neural networks. Furthermore, in the sensitivity analysis, feed solution (FS) and draw solution (DS) concentrations showed a strong correlation with membrane flux. PRACTITIONER POINTS: The FO membrane flux was predicted using a variety of machine-learning models. Thorough data preprocessing was executed. The XGBoost model showed the best performance, with an R2 of 0.992. Tree-based models outperformed neural networks and other models.

Modeling CO2 solubility in water using gradient boosting and light gradient boosting machine.

The growing application of carbon dioxide (CO2) in various environmental and energy fields, including carbon capture and storage (CCS) and several CO2-based enhanced oil recovery (EOR) techniques, highlights the importance of studying the phase equilibria of this gas with water. Therefore, accurate prediction of CO2 solubility in water becomes an important thermodynamic property. This study focused on developing two powerful intelligent models, namely gradient boosting (GBoost) and light gradient boosting machine (LightGBM) that predict CO2 solubility in water with high accuracy. The results revealed the outperformance of the GBoost model with root mean square error (RMSE) and determination coefficient (R2) of 0.137 mol/kg and 0.9976, respectively. The trend analysis demonstrated that the developed models were highly capable of detecting the physical trend of CO2 solubility in water across various pressure and temperature ranges. Moreover, the Leverage technique was employed to identify suspected data points as well as the applicability domain of the proposed models. The results showed that less than 5% of the data points were detected as outliers representing the large applicability domain of intelligent models. The outcome of this research provided insight into the potential of intelligent models in predicting solubility of CO2 in pure water.

Dynamical steps that bridge piecewise adiabatic shapes in the exact time-dependent potential energy surface.

We study the exact time-dependent potential energy surface (TDPES) in the presence of strong nonadiabatic coupling between the electronic and nuclear motion. The concept of the TDPES emerges from the exact factorization of the full electron-nuclear wave function [A. Abedi, N. T. Maitra, and E. K. U. Gross, Phys. Rev. Lett. 105, 123002 (2010)]. Employing a one-dimensional model system, we show that the TDPES exhibits a dynamical step that bridges between piecewise adiabatic shapes. We analytically investigate the position of the steps and the nature of the switching between the adiabatic pieces of the TDPES.

Saponin protects against cyclophosphamide-induced kidney and liver damage via antioxidant and anti-inflammatory actions.

Background: The liver and kidney are organs affected by chemotherapy drugs such as cyclophosphamide (CP). This study examined the protective effects of treatment with saponin (SP) against CP-induced nephrotoxicity and hepatotoxicity. Methods: 24 adult male mice were divided into four groups (N = 6): Control group, CP (15 mg kg-1), SP (2.5 mg kg-1) and CP + SP. After treatment, blood samples were collected for the determination of biochemical parameters. Liver and kidney samples were taken for histological analysis and assessment of oxidative stress and inflammatory markers. Results: Cyclophosphamide decreased renal and liver functions and antioxidant enzymes, which significantly increased blood urea nitrogen and creatinine (BUN, Cr), liver enzyme levels, malondialdehyde, nuclear factor kappa ß (NF-kB) and Interleukin 1 beta (IL-1B) concentrations. Moreover, histopathological findings of the CP group showed that there were acute tubular necrosis and glomerular atrophy in the renal tissues and lymphocyte infiltration in the liver samples. Treatment with saponin improved hepatic and renal functions, pathological changes and antioxidant capacity, and also decreased lipid peroxidation and inflammation. Conclusion: It seems that saponin could exert a hepato-nephroprotective effect against cyclophosphamide toxicity.

Sleep Signal Analysis for Early Detection of Alzheimer's Disease and Related Dementia (ADRD).

OBJECTIVE: Alzheimer's Disease and Related Dementia (ADRD) is growing at alarming rates, putting research and development of diagnostic methods at the forefront of the biomedical research community. Sleep disorder has been proposed as an early sign of Mild Cognitive Impairment (MCI) in Alzheimer's disease. Although several clinical studies have been conducted to assess sleep and association with early MCI, reliable and efficient algorithms to detect MCI in home-based sleep studies are needed in order to address both healthcare costs and patient discomfort in hospital/lab-based sleep studies. METHODS: In this paper, an innovative MCI detection method is proposed using an overnight recording of movements associated with sleep combined with advanced signal processing and artificial intelligence. A new diagnostic parameter is introduced which is extracted from the correlation between high frequency, sleep-related movements and respiratory changes during sleep. The newly defined parameter, Time-Lag (TL), is proposed as a distinguishing criterion that indicates movement stimulation of brainstem respiratory regulation that may modulate hypoxemia risk during sleep and serve as an effective parameter for early detection of MCI in ADRD. By implementing Neural Networks (NN) and Kernel algorithms with choosing TL as the principle component in MCI detection, high sensitivity (86.75% for NN and 65% for Kernel method), specificity (89.25% and 100%), and accuracy (88% and 82.5%) have been achieved.