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A parametric investigation of binaural beats for brain entrainment and enhancing sustained attention
Weak Covalent Bonds and Mechanochemistry for Synergistic Self-Strengthening of Elastomers
Microbubble dynamics in brain microvessels
Focused ultrasound stimulation of microbubbles is being tested in clinical trials for its ability to deliver drugs across the blood-brain barrier (BBB). This technique has the potential to treat neurological diseases by preferentially delivering drugs to targeted regions. Yet despite its potential, the physical mechanisms by which microbubbles alter the BBB permeability remain unclear, as direct observations of microbubbles oscillating in brain microvessels have never been previously recorded. The purpose of this study was to reveal how microbubbles respond to ultrasound when within the microvessels of living brain tissue. Microbubbles in acute brain slices acquired from juvenile rats perfused with a concentrated solution of SonoVue® and dye were exposed to ultrasound pulses typically used in BBB disruption (center frequency: 1 MHz, peak-negative pressure: 0.2–1 MPa, pulse length: up to 10 ms) and observed using high-speed microscopy at up to 10 million frames per second. We observed that microbubbles can exert mechanical stresses on a wide region of tissue beyond their initial location and immediate surroundings. A single microbubble can apply mechanical stress to parenchymal tissues several micrometers away from the vessel. Microbubbles can travel at high velocities within the microvessels, extending their influence across tens of micrometers during a single pulse. With longer pulses and higher pressures, microbubbles could penetrate the vessel wall and move through the parenchyma. The probability of extravasation scales approximately with mechanical index, being rare at low pressures, but much more common at a mechanical index ≥ 0.6. These results present the first direct observations of ultrasound-driven microbubbles within brain tissue, and illustrate a range of microbubble behaviors that have the potential to lead to safe drug delivery or tissue damage.
Body image of men and women with congenital heart disease over a 15 years observational period
Abstract Most studies devoted to the psychological consequences of congenital heart disease (CHD) have dealt with consequences in terms of psychopathology. We wanted to consider two specific aspects of body image, “Rejecting body evaluation” and “Vital body dynamics”. We examined body image of CHD-patients as compared with the general population, the stability of body image of patients over time, and the relationship of body image with disease severity. The study combined a longitudinal (panel-) and a case-control design. The findings were based on a long-term study of CHD-patients with two surveys about 15 years apart with N = 244 who participated in both. The control group consisted of the same number of cases matched by age, gender and education drawn from a national survey designed to examine body image in the general population. More men than women were classified into the group with severe CHD. Body image differences between CHD-patients and controls were found only in men, but not in women, and they emerged primarily in terms of vitality. The two dimensions of body image turned out as moderately to strongly stable over time, relationships between disease severity and body image emerged only for perceived vitality. Contrary to expectation, effects of age and sex were largely absent. The body image of men turned out to be more affected by congenital heart disease than women. Body image is not stable, but it is changing with increasing age, and disease severity is affecting body image only in terms of the perception of physical performance.
Mechanically Triggered Protein Desulfurization
Power beacon-assisted energy harvesting symbiotic radio networks: Outage performance
The evolution of next-generation Internet-of-Things (IoT) in recent years exhibits a unique segment that wireless communication paradigms are oriented towards not only improved spectral efficiency transmission but also energy efficiency. This paper addresses these critical issues by proposing a novel communication model, namely power beacon-assisted energy-harvesting symbiotic radio. In particular, the limited energy primary IoT source communicates with its destination by first harvesting energy from a dedicated power beacon and then performing information exchange, while the backscatter device communicates by exploiting the available radio frequency emitted by the primary IoT source. The destination uses successive interference cancellation mechanisms to decode both its received signals. To assess the performance quality of the proposed communication model, we theoretically derive the coexistence outage probability (COP) in terms of highly accurate expressions and upper-bound and lower-bound approximations. Subsequently, we carry out a series of numerical results to verify the developed theory frameworks on the one hand, and on the other hand, analyze the COP performance against the variations of system key parameters (transmit signal-to-noise ratio, the time-splitting coefficient, the energy conversion efficiency factor, the reflection coefficient, and the coexistent decoding threshold). Our numerical results demonstrate that the proposed communication model can potentially work well in practices with reliable communication over 90% (COP is less than 0.1). Additionally, it also demonstrates that optimizing the reflection coefficient at the backscatter device can facilitate achieving minimal COP performance.
Orally administered organophosphorus dimethoate mediated biochemical alterations in male and female experimental Rattus norvegicus (albino rats)
Treatment with the CCR5 antagonist OB-002 reduces lung pathology, but does not prevent disease in a Syrian hamster model of SARS-CoV-2 infection
Since the emergence of SARS-CoV-2 and the COVID-19 pandemic, a wide range of treatment options have been evaluated in preclinical studies and clinical trials, with several being approved for use in humans. Immunomodulatory drugs have shown success in dampening the deleterious inflammatory response seen in severe COVID-19 patients, but there remains an urgent need for development of additional therapeutic options for COVID-19 treatment. A potential drug target is the CCR5-CCL5 axis, and blocking this pathway may protect against severe disease. Here we evaluated whether OB-002, an analog of human CCL5 and a potent antagonist of CCR5, provides therapeutic benefit in SARS-CoV-2 infected Syrian hamsters. Daily treatment with OB-002 altered immune gene transcription in the lungs, and reduced pathology following infection, but did not prevent weight loss or viral replication in the lungs of infected animals, even in combination with the antiviral drug remdesivir. Our data suggest that targeting the CCR5-CCL5 pathway in SARS-CoV-2 infection in hamsters is insufficient to significantly impact disease development in this model.
Mycochemistry, antioxidant, anticancer activity, and molecular docking of compounds of F12 of ethyl acetate extract of Astraeus asiaticus with BcL2 and Caspase 3
Allele-specific methylation of SSTR4 associated with aging and cognitive functions in patients with schizophrenia
The co-occurrence of alcohol use disorder (AUD) and schizophrenia is prevalent, with a rate of 33.7%. Previous research has suggested a genetic and epigenetic overlap between these two disorders. SSTR4, a member of the somatostatin receptor family, is implicated in various neurological and psychiatric conditions, including cognitive function, AUD, and schizophrenia. However, the role of genetic-epigenetic interactions involving SSTR4 in patients with schizophrenia remains unexplored. In this study, we conducted an integration of publicly available datasets and identified allele-specific methylation patterns in SSTR4. Additionally, we pinpointed several genetic variants (rs17691954, rs11464356, rs3109190, and rs145879288) that influence the pace of aging and cognitive functions (rs705935) through their quantitative trait loci effects on CpG sites within SSTR4.
Establishment and characterization of ovarian clear cell carcinoma patient-derived xenografts
Steering Energy Transfer Pathways through Mn-Doping in Perovskite Nanocrystals
Effects of zooplankton abundance on the spawning phenology of winter-spawning Downs herring (Clupea harengus)
We have investigated phenological shifts in autumn- and winter-spawning Atlantic herring (Clupea harengus) in the Eastern English Channel and the Southern North Sea (Downs component), in relation to temperature and the availability of potential zooplanktonic prey (Calanus finmarchicus, Calanus helgolandicus, Temora longicornis). A two-tiered approach building on the monthly distribution of commercial herring landings was developed, which consisted of, (1) calculating the timing and duration of spawning season based on estimated deviations from basic harmonic signals and, (2) analysing their inter-annual variations in relation to biotic (zooplankton abundance) and abiotic (temperature) environmental variables through time series analyses. The start, midpoint and ending of herring spawning season were increasingly delayed over the period 1999–2021, a process which was correlated with the abundance of Calanus finmarchicus. The resulting duration of spawning season slightly decreased. Direct effects of sea temperatures on any phenological metrics could not be clearly evidenced. Different ecological processes were likely involved in the start and ending of spawning season. Additional covariates (including size/age composition, the biotic and abiotic factors other than those examined in our study) could contribute to a better explanation of the phenological drift in Downs herring spawning.
Correlation of ferritin and glutathione peroxidase 4 (GPX4) level as a marker of ferroptosis process in endometrioma
Abstract Endometriosis is a chronic inflammatory disease characterized by the presence of endometrial-like tissues (glands and stroma) located outside the uterine cavity. The pathophysiology of this condition remains incompletely understood. Local bleeding and inflammation within endometriosis lesions, which occur in an environment rich in iron, reactive oxygen species (ROS), and free radicals, can disturb the balance of iron within the peritoneal cavity. This disruption can trigger oxidative injury and an inflammatory response, leading to ferroptosis, particularly in the endometrioma phenotype. This research utilized an observational analytical method with a cross-sectional design to examine the relationship between ferritin and GPx4 levels by assessing them simultaneously. The data collection occurred at Dr. Hasan Sadikin Hospital, Cibabat Hospital, Bandung Kiwari Hospital, and Limijati Women and Children Hospital Bandung. Observational analytic data were gathered between February and July 2023 from female patients diagnosed with endometrioma who underwent either laparoscopic or laparotomy surgery, either for therapeutic or diagnostic reasons. There were 58 patients who met the inclusion and exclusion criteria in this study. A significant correlation was observed between ferritin and glutathione peroxidase 4 (GPx4) levels with a correlation coefficient of -0.600 (p < 0.001). However, there was no significant correlation between both ferritin levels and the severity of endometriosis (based on Association of Gynecologic Laparoscopists staging). There was significant correlation between ferritin and glutathione peroxidase 4 (GPx4) levels.
From Radical Coupling to Enantioselective Controlled Protonation: Advancing Precise Construction of Stereocenters
Potential improvement in spatial accessibility of methadone treatment with integration into other outpatient substance use disorder treatment programs, New York City, 2024
Background Methadone is an effective treatment for opioid use disorder; however, its provision in the US is limited to federally-regulated opioid treatment programs (OTP). Expansion of methadone treatment into non-OTP substance use disorder (SUD) treatment programs (‘expanded methadone treatment access’) is a promising intervention to increase access. Methods We performed a cross-sectional geospatial analysis of public transit times to OTPs, expanded methadone treatment access, and other healthcare facilities as of March, 2024 in New York City (NYC). We estimated one-way public transit travel time and compared travel times using population weighted paired t-tests. Results For OTPs, 38.2% (95% CI: 38.0, 38.4) of the NYC population was within 15 minutes and 79.7% (95% CI: 79.5, 79.9) was within 30 minutes. For expanded methadone treatment access, 72.1% (95% CI: 71.9, 72.2) of the NYC population was within 15 minutes and 97.5% (95% CI: 97.5, 97.6) was within 30 minutes. The mean travel time was 20.4 minutes (SD: 10.9) for OTPs and 12.1 minutes (SD: 7.1) for expanded methadone treatment access (difference: -8.3 minutes [95% CI: -8.5, -8.1]; P < 0.001). The mean travel time for expanded methadone treatment access was slightly longer than the mean travel time for dialysis facilities (difference: 0.22 minutes [95% CI: 0.06, 0.39]; P = 0.009]), not significantly different than Federally Qualified Health Centers (difference: -0.06 minutes [95% CI: -0.22, 0.11]; P = 0.51), and significantly shorter than the mean travel time to ambulatory surgical centers (difference: -6.3 [95% CI: -6.5, -6.0]; P < 0.001) and hospitals (difference: -8.1 [95% CI: -8.3, -7.9]; P < 0.001). Conclusion Efforts to increase access to methadone treatment in the US should promote expansion to additional non-OTP outpatient SUD treatment programs. Such integration is anticipated to increase spatial accessibility of methadone treatment substantially, greatly enhancing the potential for patient access.
Machine learning for classifying chronic kidney disease and predicting creatinine levels using at-home measurements
Abstract Chronic kidney disease (CKD) is a global health concern with early detection playing a pivotal role in effective management. Machine learning models demonstrate promise in CKD detection, yet the impact on detection and classification using different sets of clinical features remains under-explored. In this study, we focus on CKD classification and creatinine prediction using three sets of features: at-home, monitoring, and laboratory. We employ artificial neural networks (ANNs) and random forests (RFs) on a dataset of 400 patients with 25 input features, which we divide into three feature sets. Using 10-fold cross-validation, we calculate metrics such as accuracy, true positive rate (TPR), true negative rate (TNR), and mean squared error. Our results reveal RF achieves superior accuracy (92.5%) in at-home CKD classification over ANNs (82.9%). ANNs achieve a higher TPR (92.0%), but a lower TNR (67.9%) compared with RFs (90.0% and 95.8%, respectively). For monitoring and laboratory features, both methods achieve accuracies exceeding 98%. The R2 score for creatinine regression is approximately 0.3 higher with laboratory features than at-home features. Feature importance analysis identifies the key clinical variables hemoglobin and blood urea, and key comorbidities hypertension and diabetes mellitus, in agreement with previous studies. Machine learning models, particularly RFs, exhibit promise in CKD diagnosis and highlight significant features in CKD detection. Moreover, such models may assist in screening a general population using at-home features—potentially increasing early detection of CKD, thus improving patient care and offering hope for a more effective approach to managing this prevalent health condition.