From pixels to druggable leads: A CADD strategy for the design and synthesis of potent DDR1 inhibitors

BACKGROUND AND OBJECTIVE

While numerous in silico tools exist for target-based drug discovery, the inconsistent integration of in vitro data with predictive models hinders research and development productivity. This is particularly apparent during the Hit-to-Lead stage, where unreliable in-silico tools often lead to suboptimal lead selection. Herein, we address this challenge by presenting a CADD-guided pipeline that successfully integrates rational drug design with in-silico hits to identify a promising DDR1 lead.

METHODS

2 × 1000 ns MD simulations along with their respective FEL and MMPBSA analyses were employed to guide the rational design and synthesis of 12 novel compounds which were evaluated for their DDR inhibition.

RESULTS

The molecular dynamics investigation of the initial hit led to the identification of key structural features within the DDR1 binding pocket. The identified key features were used to guide the rational design and synthesis of twelve novel derivatives. SAR analysis, biological evaluation, molecular dynamics, and free energy calculations were carried out for the synthesized derivatives to understand their mechanism of action. Compound 4c exhibited the strongest inhibition and selectivity for DDR1, with an IC50 of 0.11 µM.

CONCLUSIONS

The MD simulations led to the identification of a key hydrophobic groove in the DDR1 binding pocket. The integrated approach of SAR analysis with molecular dynamics led to the identification of compound 4c as a promising lead for further development of potent and selective DDR1 inhibitors. Moreover, this work establishes a protocol for translating in silico hits to real world bioactive druggable leads.

Learning to Estimate Heart Rate From Accelerometer and User's Demographics During Physical Exercises

Getting prompt insights about health and well-being in a non-invasive way is one of the most popular features available on wearable devices. Among all vital signs available, heart rate (HR) monitoring is one of the most important since other measurements are based on it. Real-time HR estimation in wearables mostly relies on photoplethysmography (PPG), which is a fair technique to handle such a task. However, PPG is vulnerable to motion artifacts (MA). As a consequence, the HR estimated from PPG signals is strongly affected during physical exercises. Different approaches have been proposed to deal with this problem, however, they struggle to handle exercises with strong movements, such as a running session. In this paper, we present a new method for HR estimation in wearables that uses an accelerometer signal and user demographics to support the HR prediction when the PPG signal is affected by motion artifacts. This algorithm requires a tiny memory allocation and allows on-device personalization since the model parameters are finetuned in real time during workout executions. Also, the model may predict HR for a few minutes without using a PPG, which represents a useful contribution to an HR estimation pipeline. We evaluate our model on five different exercise datasets - performed on treadmills and in outdoor environments - and the results show that our method can improve the coverage of a PPG-based HR estimator while keeping a similar error performance, which is particularly useful to improve user experience.

An automatic variable laser power attenuator for continuous-wave quantum cascade lasers in cryogenic ion vibrational predissociation spectroscopy

Cryogenic ion vibrational predissociation (CIVP) spectroscopy is an established and valuable technique for molecular elucidation in the gas phase. CIVP relies on tunable lasers, wherein among typical laser schemes, the application of mid-infrared continuous-wave quantum cascade laser (cw-QCL) is the most robust and elegant solution, as we have recently demonstrated. However, potential challenges arise from an inhomogeneous character across laser power tuning curves. A large laser power output could have undesired consequences, such as multiphoton absorption or saturation effects. Significant variations in laser power tuning curves could potentially alter the shape of the investigated band, particularly for diffuse bands. In this study, we have developed and introduced an automatic variable laser power attenuator designed to keep the laser power output uniform at a user-defined value across the entire available spectral range. We demonstrated the application of this attenuator in obtaining CIVP spectra of a model compound with a diffuse N-H-N band. This approach enhances the reliability of measuring diffuse bands and overall applicability of cw-QCL.

Bayesian network analysis of risk classification strategies in the regulation of cellular products

Cell therapy, a burgeoning therapeutic strategy, necessitates a scientific regulatory framework but faces challenges in risk-based regulation due to the lack of a global consensus on risk classification. This study applies Bayesian network analysis to compare and evaluate the risk classification strategies for cellular products proposed by the Food and Drug Administration (FDA), Ministry of Health, Labour and Welfare (MHLW), and World Health Organization (WHO), using real-world data to validate the models. The appropriateness of key risk factors is assessed within the three regulatory frameworks, along with their implications for clinical safety. The results indicate several directions for refining risk classification approaches. Additionally, a substudy focuses on a specific type of cell and gene therapy (CGT), chimeric antigen receptor (CAR) T cell therapy. It underscores the importance of considering CAR targets, tumor types, and costimulatory domains when assessing the safety risks of CAR T cell products. Overall, there is currently a lack of a regulatory framework based on real-world data for cellular products and a lack of risk-based classification review methods. This study aims to improve the regulatory system for cellular products, emphasizing risk-based classification. Furthermore, the study advocates for leveraging machine learning in regulatory science to enhance the assessment of cellular product safety, illustrating the role of Bayesian networks in aiding regulatory decision-making for the risk classification of cellular products.

HCoV-NL63 and HCoV-HKU1 seroprevalence and its relationship with the clinical features of COVID-19 patients from Villavicencio, Colombia

INTRODUCTION

Due to the cross-reactivity between SARS-CoV-2 and common human coronaviruses, previous infections with these viruses could contribute to serological or cellular cross-protection against severe COVID-19. However, protective immunity may not develop, or pre-existing immunity could increase COVID-19 severity.

OBJECTIVE

To determine the seroprevalence of IgG antibodies against HCoV-NL63 and HCoV-HKU1 and correlate previous exposure with COVID-19 signs in patients from Villavicencio.

MATERIALS AND METHODS

A cross-sectional retrospective study was conducted. ELISA technique was used to search for IgG antibodies against HCoV-NL3 and HCoV-HKU1 in patients with positive RT-qPCR results for SARS-CoV-2. Patients were grouped according to COVID-19 clinical characteristics in four groups: group 1: asymptomatic (n = 23); group 2: hospitalized (n = 24); group 3: intensive care units (n = 24), and group 4: dead (n = 22).

RESULTS

The overall seroprevalence of IgG antibodies against HCoV was 74.2% (n = 69; 95% CI: 65.3-83.1), with 66.7% of HCoV-NL63 (n = 62; 95% CI: 57,1-76,2), and 25.8% of HCoV-HKU1 (n = 24; 95% CI: 16,9-34,7). Based on crosstab analysis, prior exposure to HCoV-NL63 was associated with protection against severe COVID-19 (p = 0.042; adjusted OR = 0.159; 95% CI: 0.027-0.938), and previous coinfection of HCoV-NL63 and HCoVHKU1 was considered a positive association to severe COVID-19 (p = 0.048; adjusted OR = 16.704; 95% CI: 1.020 - 273.670).

CONCLUSION

To our knowledge, this is the first study addressing seroprevalence of HCoV IgG antibodies in Colombia and Latin America. Previous exposure to HCoV-NL63 could protect against severe COVID-19, whereas patients with underlying HCoV-NL63 and HCoVHKU1 coinfection could be hospitalized with severe signs of COVID-19.

Effect of Ce Addition on Microstructure, Thermal Conductivity, and Mechanical Properties of As-Cast and As-Extruded Mg-3Sn Alloys

In the present research, the impacts of Ce additions at various concentrations (0, 1.0, 3.4, and 4.0 wt.%) on the evolution of the microstructure, mechanical properties, and thermal conductivity of as-cast and as-extruded Mg-3Sn alloys were investigated. The findings demonstrate that adding Ce caused the creation of a new ternary MgSnCe phase in the magnesium matrix. Some new Mg17Ce2 phases are generated in the microstructure when Ce levels reach 4%. The thermal conductivity of the Mg-3Sn alloy is significantly improved due to Ce addition, and the Mg-3Sn-3.4Ce alloy exhibits the highest thermal conductivity, up to 133.8 W/(m·K) at 298 K. After extrusion, both the thermal conductivity and mechanical properties are further improved. The thermal conductivity perpendicular to the extrusion direction of Mg-3Sn-3.4Ce alloy could achieve 136.28 W/(m·K), and the tensile and yield strengths reach 264.3 MPa and 227.2 MPa, with an elongation of 7.9%. Adding Ce decreases the dissolved Sn atoms and breaks the eutectic α-Mg and Mg2Sn network organization, leading to a considerable increase in the thermal conductivity of as-cast Mg-3Sn alloys. Weakening the deformed grain texture contributed to the further enhancement of the thermal conductivity after extrusion.

Springback characteristics and influencing laws of four-axis flexible roll bending forming for aluminum alloy

This study aims to solve the problem of springback control of aluminum alloy components in the rolling process, and the method of combining experiment and simulation is adopted. Firstly, a series of aluminum alloy samples are designed, and the four-axis flexible bending machine is used for precision roll bending. Secondly, the three-dimensional (3D) shape change data of the workpiece before and after roll bending is monitored and recorded in real-time by a high-precision 3D scanner. Meanwhile, aiming at different rolling process parameters of each group (including roll bend speed, feed rate, pre-deformation amount, mold curvature radius, and other factors), advanced finite element software is used to carry out detailed simulation and calculations. In addition, the coincidence is compared and analyzed between the actual experiment results and the simulation prediction. The stress-strain distribution and springback evolution of aluminum alloy during roll bending are described accurately. The experimental and simulation results show that the springback rate of aluminum alloy fluctuates in the range of 5% to 15% after four-axis flexible roll bending, and the specific springback value is influenced by various process parameters. For example, under the premise of keeping other conditions unchanged, when the roll bending speed is increased from 30mm/s to 60mm/s, the springback rate shows an upward trend of about 3%. By increasing the feed rate by 20%, an average decrease of about 7% in springback quantity is observed. It can be seen that the increase in roll bending speed can aggravate the springback phenomenon, and the appropriate increase in feed rate can play a certain role in restraining the springback. Further analysis shows that the choice of the mold curvature radius and pre-deformation amount also has a decisive influence on the springback characteristics. There is a nonlinear relationship between the two parameters and the amount of springback. Changing these two parameters in a specific range can effectively regulate the springback effect.

Digital twins in dermatology, current status, and the road ahead

Digital twins, innovative virtual models synthesizing real-time biological, environmental, and lifestyle data, herald a new era in personalized medicine, particularly dermatology. These models, integrating medical-purpose Internet of Things (IoT) devices, deep and digital phenotyping, and advanced artificial intelligence (AI), offer unprecedented precision in simulating real-world physical conditions and health outcomes. Originating in aerospace and manufacturing for system behavior prediction, their application in healthcare signifies a paradigm shift towards patient-specific care pathways. In dermatology, digital twins promise enhanced diagnostic accuracy, optimized treatment plans, and improved patient monitoring by accommodating the unique complexities of skin conditions. However, a comprehensive review across PubMed, Embase, Web of Science, Cochrane, and Scopus until February 5th, 2024, underscores a significant research gap; no direct studies on digital twins' application in dermatology is identified. This gap signals challenges, including the intricate nature of skin diseases, ethical and privacy concerns, and the necessity for specialized algorithms. Overcoming these barriers through interdisciplinary efforts and focused research is essential for realizing digital twins' potential in dermatology. This study advocates for a proactive exploration of digital twins, emphasizing the need for a tailored approach to dermatological care that is as personalized as the patients themselves.

Phytochemical Profile and Antidiabetic, Antioxidant, and Anti-Inflammatory Activities of Gypsophila paniculata Ethanol Extract in Rat Streptozotocin-Induced Diabetes Mellitus

The present study aimed to investigate the effects of the Gypsophila paniculata ethanol extract (GPEE) on oxidative stress, inflammation, and metabolic markers in a rat model of streptozotocin-induced diabetes mellitus (DM). Phytochemical analysis using high-performance liquid chromatography coupled with mass spectrometry was performed to measure the total phenolic and flavonoid contents. In vitro antioxidant activity was evaluated through DPPH, FRAP, H2O2, and NO scavenging tests, and the in vivo effects of the GPEE were assessed in streptozotocin-induced DM rats. Treatments with the GPEE, metformin, and Trolox were administrated by gavage for 10 days. On day 11, blood was collected, and serum oxidative stress (total oxidative status, oxidative stress index, malondialdehyde, advanced oxidation protein products, 8-hydroxydeoxyguanosine, nitric oxide, 3-nitrotyrosine, advanced glycation end-products, total antioxidant reactivity, total thiols), inflammatory (IL-1β, NF-κB, IL-18, and gasdermin D), metabolic (fasting glucose, total cholesterol, triglycerides, and triglyceride-glucose index), and liver injury (AST, ALT, and AST:ALT ratio) markers were measured. The GPEE was found to have a significant polyphenols content and a moderate in vitro antioxidant effect. In vivo, the GPEE lowered oxidants and increased antioxidants, decreased inflammatory markers and blood glucose, and improved lipid profiles and transaminases in a dose-dependent manner, with higher doses having a better effect, being comparable to those of metformin and Trolox.

Training of Community Health Workers in Diabetes Lead to Improved Outcomes for Diabetes Screening and Management in Low- and Middle-Income Countries: Protocol for a Systematic Review

BACKGROUND

Diabetes is a growing concern worldwide, particularly in low- and middle-income countries (LMICs). Type 2 diabetes mellitus constitutes a significant proportion of cases and is associated with debilitating microvascular complications. Type 2 diabetes mellitus is steadily increasing among the LMICs where many barriers to health care exist. Thus, task shifting to community health workers (CHWs) has been proposed as a solution to improve diabetes management in these settings. However, CHWs often lack the necessary training to manage diabetes effectively. Thus, a systematic review is required to present evidence of the highest degree for this intervention.

OBJECTIVE

This study aims to establish the protocols for a systemic review.

METHODS

Using the Participants Intervention Comparator Outcome Time Study Design (PICOTS) framework, this study outlines a systematic review aiming to evaluate the impact of training programs for CHWs in diabetes management in LMICs. Quantitative studies focusing on CHWs, diabetes training, focusing on diabetes management outcomes like hemoglobin A1c levels and fasting blood glucose levels, between January 2000 and December 2023 and found on databases such as PubMed, Ovid MEDLINE, Evidence Based Medicine Reviews, BASE, Google Scholar, and Web of Science will be included. We will include randomized controlled trials but will also include observational studies if we find less than 5 randomized controlled trials. An author committee consisting of 3 reviewers will be formed, where 2 reviewers will conduct the review independently while the third will resolve all disputes. The Cochrane Methods Risk of Bias Tool 2 will be used for assessing the risk of bias and the Grading of Recommendations, Assessment, Development and Evaluation approach for the meta-analysis and narrative synthesis analysis will be used. The results will be presented in a PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) diagram.

RESULTS

The review will begin in May 2024 and conclude in 3 months.

CONCLUSIONS

The review will synthesize existing evidence and provide insights into the effectiveness of such programs, informing future research and practice in diabetes care in LMICs.

TRIAL REGISTRATION

PROSPERO CRD42022341717; https://tinyurl.com/jva2hpdr.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/57313.

Collagen-hydroxyapatite based scaffolds for bone trauma and regeneration: recent trends and future perspectives

Regenerative therapy, a key area of tissue engineering, holds promise for restoring damaged organs, especially in bone regeneration. Bone healing is natural to the body but becomes complex under stress and disease. Large bone deformities pose significant challenges in tissue engineering. Among various methods, scaffolds are attractive as they provide structural support and essential nutrients for cell adhesion and growth. Collagen and hydroxyapatite (HA) are widely used due to their biocompatibility and biodegradability. Collagen and nano-scale HA enhance cell adhesion and development. Thus, nano HA/collagen scaffolds offer potential solutions for bone regeneration. This review focuses on the use and production of nano-sized HA/collagen composites in bone regeneration.

Travel-mode inference based on GPS-trajectory data through multi-scale mixed attention mechanism

Identifying travel modes is essential for modern urban transportation planning and management. Recent advancements in data collection, especially those involving Global Positioning System (GPS) technology, offer promising opportunities for rapidly and accurately inferring users' travel modes. This study presents an innovative method for inferring travel modes from GPS trajectory data. The method utilizes multi-scale convolutional techniques to capture and analyze both temporal and spatial information of the data, thereby revealing the underlying spatiotemporal relationships inherent in user movement and behavior patterns. In addition, an attention mechanism is integrated into the model to enable autonomous learning. This mechanism enhances the model's capacity to identify and emphasize key information across different time periods and spatial locations, thus improving the accuracy of travel mode inference. Evaluation on the open-source GPS trajectory dataset, GeoLife, demonstrates that the proposed method attained an accuracy of 83.3%. This result highlights the effectiveness of the method, demonstrating that the model can more accurately understand and predict user travel modes through the integration of multi-scale convolutional technologies and attention mechanisms.

Identifying the Drivers Related to Animal Reservoirs, Environment, and Socio-Demography of Human Leptospirosis in Different Community Types of Southern Chile: An Application of Machine Learning Algorithm in One Health Perspective

Leptospirosis is a zoonosis with global public health impact, particularly in poor socio-economic settings in tropical regions. Transmitted through urine-contaminated water or soil from rodents, dogs, and livestock, leptospirosis causes over a million clinical cases annually. Risk factors include outdoor activities, livestock production, and substandard housing that foster high densities of animal reservoirs. This One Health study in southern Chile examined Leptospira serological evidence of exposure in people from urban slums, semi-rural settings, and farm settings, using the Extreme Gradient Boosting algorithm to identify key influencing factors. In urban slums, age, shrub terrain, distance to Leptospira-positive households, and neighborhood housing density were contributing factors. Human exposure in semi-rural communities was linked to environmental factors (trees, shrubs, and lower vegetation terrain) and animal variables (Leptospira-positive dogs and rodents and proximity to Leptospira-positive households). On farms, dog counts, animal Leptospira prevalence, and proximity to Leptospira-contaminated water samples were significant drivers. The study underscores that disease dynamics vary across landscapes, with distinct drivers in each community setting. This case study demonstrates how the integration of machine learning with comprehensive cross-sectional epidemiological and geospatial data provides valuable insights into leptospirosis eco-epidemiology. These insights are crucial for informing targeted public health strategies and generating hypotheses for future research.

Noncovalent Interactions and Transverse Vibration Induced Restricted Thermal Expansion of Organic Salts Derived from Imidazole and Carboxylic Acids

Design of material showing contraction upon heating is highly challenging due to varying mechanism. However, imidazole is found to be a potential molecule that may provide low CTE materials when incorporated in the matrix. Here we have reported thermal expansion property of imidazolium salts of five aliphatic α, ω-alkane dicarboxylic acids and three aromatic acids. Either uniaxial or biaxial negative thermal expansion (NTE) has been observed in most of the salts. In some cases, axial zero thermal expansion (ZTE) has been observed. The role of imidazolium moiety for the anomalous thermal expansion behaviour of the salts has been analyzed in this study. The controlled TE behaviour of the salts is attributed to the hydrogen bonding and transverse vibration in all imidazolium salts. Owing to the high transverse vibration observed in imidazolium ion as well as the heavier oxygen atoms of acids in each case, the distance between hydrogen bonded atoms decreases-which provides either low expansion or contraction along one of the principal axes.

Tailored Bioactive Glass Coating: Navigating Devitrification Toward a Superior Implant Performance

The development of well-adherent, amorphous, and bioactive glass coatings for metallic implants remains a critical challenge in biomedical engineering. Traditional bioactive glasses are susceptible to crystallization and exhibit a thermal expansion mismatch with implant materials. This study introduces a novel approach to overcome these limitations by employing systematic Na2O substitution with CaO in borosilicate glasses. In-depth structural analysis (MD simulations, Raman spectroscopy, and NMR) reveals a denser network with smaller silicate rings, enhancing thermal stability, reducing thermal expansion, and influencing dissolution kinetics. This tailored composition exhibited optimal bioactivity (in vitro formation of bone-like apatite within 3 days) and a coefficient of thermal expansion closely matching Ti-6Al-4V, a widely used implant material. Furthermore, a consolidation process, meticulously designed with insights from crystallization kinetics and the viscosity-temperature relationship, yielded a crack-free, amorphous coating on Ti-6Al-4V substrates. This novel coating demonstrates excellent cytocompatibility and strong antibacterial action, suggesting superior clinical potential compared with existing technologies.

Vaccine and antiviral drug promise for preventing post-acute sequelae of COVID-19, and their combination for its treatment

Introduction

Most healthy individuals recover from acute SARS-CoV-2 infection, whereas a remarkable number continues to suffer from unexplained symptoms, known as Long COVID or post-acute COVID-19 syndrome (PACS). It is therefore imperative that methods for preventing and treating the onset of PASC be investigated with the utmost urgency.

Methods

A mathematical model of the immune response to vaccination and viral infection with SARS-CoV-2, incorporating immune memory cells, was developed.

Results and discussion

Similar to our previous model, persistent infection was observed by the residual virus in the host, implying the possibility of chronic inflammation and delayed recovery from tissue injury. Pre-infectious vaccination and antiviral medication administered during onset can reduce the acute viral load; however, they show no beneficial effects in preventing persistent infection. Therefore, the impact of these treatments on the PASC, which has been clinically observed, is mainly attributed to their role in preventing severe tissue damage caused by acute viral infections. For PASC patients with persistent infection, vaccination was observed to cause an immediate rapid increase in viral load, followed by a temporary decrease over approximately one year. The former was effectively suppressed by the coadministration of antiviral medications, indicating that this combination is a promising treatment for PASC.

Research progress of fluorescent composites based on cyclodextrins: Preparation strategies, fluorescence properties and applications in sensing and bioimaging

Fluorescence analysis has been regarded as one of the commonly used analytical methods because of its advantages of simple operation, fast response, low cost and high sensitivity. So far, various fluorescent probes, with noble metal nanoclusters, quantum dots, organic dyes and metal organic frameworks as representatives, have been widely reported. However, single fluorescent probe often suffers from some deficiencies, such as low quantum yield, poor chemical stability, low water solubility and toxicity. To overcome these disadvantages, the introduction of cyclodextrins into fluorescent probes has become a fascinating approach. This review (with 218 references) systematically covers the research progress of fluorescent composites based on cyclodextrins in recent years. Preparation strategies, fluorescence properties, response mechanisms and applications in sensing (ions, organic pollutants, bio-related molecules, temperature, pH) and bioimaging of fluorescent composites based on cyclodextrins are summarized in detail. Finally, the current challenges and future perspectives of these composites in relative research fields are discussed.

Tale of Two Polymorphs: Investigating the Structural Differences and Dynamic Relationship between Nirmatrelvir Solid Forms (Paxlovid)

Two anhydrous polymorphs of the novel antiviral medicine nirmatrelvir were discovered during the development of Paxlovid, Pfizer's oral Covid-19 treatment. A comprehensive experimental and computational approach was necessary to distinguish the two closely related polymorphs, herein identified as Forms 1 and 4. This approach paired experimental methods, including powder X-ray diffraction and single-crystal X-ray diffraction, solid-state experimental methods, thermal analysis, solid-state nuclear magnetic resonance and Raman spectroscopy with computational investigations comprising crystal structure prediction, Gibbs free energy calculations, and molecular dynamics simulations of the polymorphic transition. Forms 1 and 4 were ultimately determined to be enantiotropically related polymorphs with Form 1 being the stable form above the transition temperature of ∼17 °C and designated as the nominated form for drug development. The work described in this paper shows the importance of using highly specialized orthogonal approaches to elucidate the subtle differences in structure and properties of similar solid-state forms. This synergistic approach allowed for unprecedented speed in bringing Paxlovid to patients in record time amidst the pandemic.

Perspectives on deprescribing in older people with type 2 diabetes and/or cardiovascular conditions: challenges from healthcare provider, patient and caregiver perspective, and interventions to support a proactive approach

INTRODUCTION

For people with type 2 diabetes and/or cardiovascular conditions, deprescribing of glucose-lowering, blood pressure-lowering and/or lipid-lowering medication is recommended when they age, and their health status deteriorates. So far, deprescribing rates of these so-called cardiometabolic medications are low. A review of challenges and interventions addressing these challenges in this population is pertinent.

AREAS COVERED

We first provide an overview of relevant deprescribing recommendations. Next, we review challenges for healthcare providers (HCPs) to deprescribe cardiometabolic medication and provide insight in the patient and caregiver perspective on deprescribing. We summarize findings from research on implementing deprescribing of cardiometabolic medication and reflect on strategies to enhance deprescribing. We have used a combination of methods to search for relevant articles.

EXPERT OPINION

There is a need for rigorous development and evaluation of intervention strategies aimed at proactive deprescribing of cardiometabolic medication. To address challenges at different levels, these should be multifaceted interventions. All stakeholders must become aware of the relevance of deintensifying medication in this population. Education and training for HCPs and patients should support patient-centered communication and shared decision-making. Development of procedures and tools to select eligible patients and conduct targeted medication reviews are important for implementation of deprescribing in routine care.

DNA sequences alignment method using sparse index on pan-genome graph

The graph of sequences represents the genetic variations of pan-genome concisely and space-efficiently than multiple linear reference genome. In order to accelerate aligning reads to the graph, an index of graph-based reference genomes is used to obtain candidate locations. However, the potential combinatorial explosion of nodes on the sequence graph leads to increasing the index space and maximum memory usage of alignment process considerably, especially for large-scale datasets. For this, existing methods typically attempt to prune complex regions, or extend the length of seeds, which sacrifices the recall of alignment algorithm despite reducing space usage slightly. We present the Sparse-index of Graph (SIG) and alignment algorithm SIG-Aligner, capable of indexing and aligning at the lower memory cost. SIG builds the non-overlapping minimizers index inside nodes of sequence graph and SIG-Aligner filters out most of the false positive matches by the method based on the pigeonhole principle. Compared to Giraffe, the results of computational experiments show that SIG achieves a significant reduction in index memory space ranging from 50% to 75% for the human pan-genome graphs, while still preserving superior or comparable accuracy of alignment and the faster alignment time.

To Prosper, Live Long: Understanding the Sources of Reproductive Skew and Extreme Reproductive Success in Structured Populations

AbstractIn many species, a few individuals produce most of the next generation. How much of this reproductive skew is driven by variation among individuals in fixed traits, how much by external factors, and how much by random chance? And what does it take to have truly exceptional lifetime reproductive output (LRO)? In the past, we and others have partitioned the variance of LRO as a proxy for reproductive skew. Here we explain how to partition LRO skewness itself into contributions from fixed trait variation, four forms of "demographic luck" (birth state, fecundity luck, survival trajectory luck, and growth trajectory luck), and two kinds of "environmental luck" (birth environment and environment trajectory). Each of these is further partitioned into contributions at different ages. We also determine what we can infer about individuals with exceptional LRO. We find that reproductive skew is largely driven by random variation in lifespan, and exceptional LRO generally results from exceptional lifespan. Other kinds of luck frequently bring skewness down rather than increasing it. In populations where fecundity varies greatly with environmental conditions, getting a good year at the right time can be an alternate route to exceptional LRO, so that LRO is less predictive of lifespan.

In Situ ATR-FTIR Studies on the β-Sheet Formation of Native and Regenerated Bombyx mori Silk Material in Solution and Its Potential for Drug Releasing Coatings

Dynamic attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy at both solutions and coatings of a semicrystalline silk material derived from Bombyx mori was applied to monitor the β-sheet conformation, which is known to correlate with silk protein crystallinity. The secondary structure-sensitive Amide I band was analyzed. Two silk protein samples were studied: native-based silk buffer fibroin (NSF) was extracted from silk glands and regenerated silk fibroin (RSF) was extracted from degummed cocoons. Solutions of both NSF and RSF at 2 mg/mL featured low initial β-sheet contents of 5-12%, which further increased to 47-53% after 24 h. RSF and NSF solutions at 23 mg/mL also featured low initial β-sheet contents of 9-10%, which yet only slightly increased to 16-17% after 24 h. Coatings deposited from RSF solutions showed high surface integrity (Q > 99%) after rinsing in mineralized water, enabling interfacial drug delivery applications. RSF coatings were post-treated with either formic acid (FA) or pure methanol (MeOH) vapor to showcase inducibility of crystalline domains in RSF coatings. Such coatings were loaded with the model antibiotic drugs tetracycline (TCL) and streptomycin (STRP), and the sustained release of TCL was followed in contact with (4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid) (HEPES) buffer. RSF/TCL coatings post-treated with formic acid (FA) vapor followed by methanol (MeOH) vapor showed a significantly lower (52%) initial burst of rather hydrophobic TCL compared to untreated RSF/TCL coatings (72%), while no such significant release difference was observed for hydrophilic STRP. This was rationalized by a specific interaction between nonpolar TCL and hydrophobic crystalline RSF domains.

Enhanced Assessment of Cross-Reactive Antigenic Determinants within the Spike Protein

Despite successful vaccination efforts, the emergence of new SARS-CoV-2 variants poses ongoing challenges to control COVID-19. Understanding humoral responses regarding SARS-CoV-2 infections and their impact is crucial for developing future vaccines that are effective worldwide. Here, we identified 41 immunodominant linear B-cell epitopes in its spike glycoprotein with an SPOT synthesis peptide array probed with a pool of serum from hospitalized COVID-19 patients. The bioinformatics showed a restricted set of epitopes unique to SARS-CoV-2 compared to other coronavirus family members. Potential crosstalk was also detected with Dengue virus (DENV), which was confirmed by screening individuals infected with DENV before the COVID-19 pandemic in a commercial ELISA for anti-SARS-CoV-2 antibodies. A high-resolution evaluation of antibody reactivity against peptides representing epitopes in the spike protein identified ten sequences in the NTD, RBD, and S2 domains. Functionally, antibody-dependent enhancement (ADE) in SARS-CoV-2 infections of monocytes was observed in vitro with pre-pandemic Dengue-positive sera. A significant increase in viral load was measured compared to that of the controls, with no detectable neutralization or considerable cell death, suggesting its role in viral entry. Cross-reactivity against peptides from spike proteins was observed for the pre-pandemic sera. This study highlights the importance of identifying specific epitopes generated during the humoral response to a pathogenic infection to understand the potential interplay of previous and future infections on diseases and their impact on vaccinations and immunodiagnostics.

The Impact of Gelatin and Fish Collagen on Alginate Hydrogel Properties: A Comparative Study

Hydrogel materials based on sodium alginate find versatile applications in regenerative medicine and tissue engineering due to their unique properties, such as biocompatibility and biodegradability, and the possibility of the customization of their mechanical properties, such as in terms of the individual requirements of separate clinical applications. These materials, however, have numerous limitations in the area of biological activity. In order to eliminate their limitations, sodium alginate is popularly applied in combination with added gelatin, which represents a product of collagen hydrolysis. Despite numerous beneficial biological properties, matrix materials based on gelatin have poor mechanical properties and are characterized by their ability for rapid degradation in an aqueous environment, particularly at the physiological temperature of the body, which significantly limits the independent application opportunities of this type of composition in the range of scaffolding production dedicated for tissue engineering. Collagen hydrogels, unlike gelatin, are characterized by higher bioactivity, dictated by a greater number of ligands that allow for cell adhesion, as well as better stability under physiological conditions. Fish-derived collagen provides a material that may be efficiently extracted without the risk of mammalian prion infection and can be used in all patients without religious restrictions. Considering the numerous advantages of collagen indicating its superiority over gelatin, within the framework of this study, the compositions of hydrogel materials based on sodium alginate and fish collagen in different concentrations were developed. Prepared hydrogel materials were compared with the properties of a typical composition of alginate with the addition of gelatin. The rheological, mechanical, and physicochemical properties of the developed polymer compositions were evaluated. The first trials of 3D printing by extrusion technique using the analyzed polymer solutions were also conducted. The results obtained indicate that replacing gelatin with fish collagen at an analogous concentration leads to obtaining materials with a lower swelling degree, better mechanical properties, higher stability, limited release kinetics of calcium ions cross-linking the alginate matrix, a slowed process of protein release under physiological conditions, and the possibility of extrusion 3D printing. The conducted analysis highlights that the optimization of the applied concentrations of fish collagen additives to composition based on sodium alginate creates the possibility of designing materials with appropriate mechanical and rheological properties and degradation kinetics adjusted to the requirements of specific applications, leading to the prospective opportunity to produce materials capable of mimicking the properties of relevant soft tissues. Thanks to its excellent bioactivity and lower-than-gelatin viscosity of the polymer solution, fish collagen also provides a prospective solution for applications in the field of 3D bioprinting.

Isosteric Replacement of Sulfur to Selenium in a Thiosemicarbazone: Promotion of Zn(II) Complex Dissociation and Transmetalation to Augment Anticancer Efficacy

We implemented isosteric replacement of sulfur to selenium in a novel thiosemicarbazone (PPTP4c4mT) to create a selenosemicarbazone (PPTP4c4mSe) that demonstrates potentiated anticancer efficacy and selectivity. Their design specifically incorporated cyclohexyl and styryl moieties to sterically inhibit the approach of their Fe(III) complexes to the oxy-myoglobin heme plane. Importantly, in contrast to the Fe(III) complexes of the clinically trialed thiosemicarbazones Triapine, COTI-2, and DpC, the Fe(III) complexes of PPTP4c4mT and PPTP4c4mSe did not induce detrimental oxy-myoglobin oxidation. Furthermore, PPTP4c4mSe demonstrated more potent antiproliferative activity than the homologous thiosemicarbazone, PPTP4c4mT, with their selectivity being superior or similar, respectively, to the clinically trialed thiosemicarbazone, COTI-2. An advantageous property of the selenosemicarbazone Zn(II) complexes relative to their thiosemicarbazone analogues was their greater transmetalation to Cu(II) complexes in lysosomes. This latter effect probably promoted their antiproliferative activity. Both ligands down-regulated multiple key receptors that display inter-receptor cooperation that leads to aggressive and resistant breast cancer.

Expanding the boundaries of silk sericin biomaterials in biomedical applications

Silk sericin (SS) has a long history as a by-product of the textile industry. SS has emerged as a sustainable material for biomedical engineering due to its material properties including water solubility, diverse impact on biological activities including antibacterial and antioxidant properties, and ability to promote cell adhesion and proliferation. This review addresses the origin, structure, properties, extraction, and underlying functions of this protein. An overview of the growing research studies and market evolution is presented, along with highlights of the most common fabrication matrices (hydrogels, bioinks, porous and fibrous scaffolds) and tissue engineering applications. Finally, the future trends with this protein as a multifaceted toolbox for bioengineering are explored, along with the challenges with SS. Overall, the present review can serve as a foundation for the creation of innovative biomaterials utilizing SS as a fundamental building block that hold market potential.

Redox-Responsive Halogen Bonding as a Highly Selective Interaction for Electrochemical Separations

Leveraging specific noncovalent interactions can broaden the mechanims for selective electrochemical separations beyond solely electrostatic interactions. Here, we explore redox-responsive halogen bonding (XB) for selective electrosorption in nonaqueous media, by taking advantage of directional interactions of XB alongisde a cooperative and synergistic ferrocene redox-center. We designed and evaluated a new redox-active XB donor polymer, poly(5-iodo-4-ferrocenyl-1-(4-vinylbenzyl)-1H-1,2,3-triazole) (P(FcTS-I)), for the electrochemically switchable binding and release of target organic and inorganic ions at a heterogeneous interface. Under applied potential, the oxidized ferrocene amplifies the halogen binding site, leading to significantly enhanced uptake and selectivity towards key inorganic and organic species, including chloride, bisulfate, and benzenesulfonate, compared to the open-circuit potential or the hydrogen bonding donor analog. Density functional theory calculations, as well as spectroscopic analysis, offer mechanistic insight into the degree of amplification of σ-holes at a molecular level, with selectivity modulated by charge transfer and dispersion interactions. Our work highlights the potential of XB in selective electrosorption by uniquely leveraging noncovalent interactions for redox-mediated electrochemical separations.

C(sp3)-H (N-Phenyltetrazole)thiolation as an Enabling Tool for Molecular Diversification

Methods enabling the broad diversification of C(sp3)-H bonds from a common intermediate are especially valuable in chemical synthesis. Herein, we report a site-selective (N-phenyltetrazole)thiolation of aliphatic and (hetero)benzylic C(sp3)-H bonds using a commercially available disulfide to access N-phenyltetrazole thioethers. The thioether products are readily elaborated in diverse fragment couplings for C-C, C-O, or C-N construction. The C-H functionalization proceeds via a radical-chain pathway involving hydrogen atom transfer by the electron-poor N-phenyltetrazolethiyl radical. Hexafluoroisopropanol was found to be essential to reactions involving aliphatic C(sp3)-H thiolation, with computational analysis consistent with dual hydrogen bonding of the N-phenyltetrazolethiyl radical imparting increased radical electrophilicity to facilitate the hydrogen atom transfer. Substrate is limiting reagent in all cases, and the reaction displays an exceptional functional group tolerance well suited to applications in late-stage diversification.

Advancements in Insulin Pumps: A Comprehensive Exploration of Insulin Pump Systems, Technologies, and Future Directions

Insulin pumps have transformed the way diabetes is managed by providing a more accurate and individualized method of delivering insulin, in contrast to conventional injection routines. This research explores the progression of insulin pumps, following their advancement from initial ideas to advanced contemporary systems. The report proceeds to categorize insulin pumps according to their delivery systems, specifically differentiating between conventional, patch, and implantable pumps. Every category is thoroughly examined, emphasizing its unique characteristics and capabilities. A comparative examination of commercially available pumps is provided to enhance informed decision making. This section provides a thorough analysis of important specifications among various brands and models. Considered factors include basal rate and bolus dosage capabilities, reservoir size, user interface, and compatibility with other diabetes care tools, such as continuous glucose monitoring (CGM) devices and so on. This review seeks to empower healthcare professionals and patients with the essential information to improve diabetes treatment via individualized pump therapy options. It provides a complete assessment of the development, categorization, and full specification comparisons of insulin pumps.

Digital Twins of Biological Systems: A Narrative Review

The concept of Digital Twins (DTs), software models that mimic the behavior and interactions of physical or conceptual objects within their environments, has gained traction in recent years, particularly in medicine and healthcare research. DTs technology emerges as a pivotal tool in disease modeling, integrating diverse data sources to computationally model dynamic biological systems. This narrative review explores potential DT applications in medicine, from defining DTs and their history to constructing DTs, modeling biologically relevant systems, as well as discussing the benefits, risks, and challenges in their application. The influence of DTs extends beyond healthcare and can revolutionize healthcare management, drug development, clinical trials, and various biomedical research fields.

Advances in 3D tissue models for neural engineering: self-assembled versus engineered tissue models

Neural tissue engineering has emerged as a promising field that aims to create functional neural tissue for therapeutic applications, drug screening, and disease modelling. It is becoming evident in the literature that this goal requires development of three-dimensional (3D) constructs that can mimic the complex microenvironment of native neural tissue, including its biochemical, mechanical, physical, and electrical properties. These 3D models can be broadly classified as self-assembled models, which include spheroids, organoids, and assembloids, and engineered models, such as those based on decellularized or polymeric scaffolds. Self-assembled models offer advantages such as the ability to recapitulate neural development and disease processes in vitro, and the capacity to study the behaviour and interactions of different cell types in a more realistic environment. However, self-assembled constructs have limitations such as lack of standardised protocols, inability to control the cellular microenvironment, difficulty in controlling structural characteristics, reproducibility, scalability, and lengthy developmental timeframes. Integrating biomimetic materials and advanced manufacturing approaches to present cells with relevant biochemical, mechanical, physical, and electrical cues in a controlled tissue architecture requires alternate engineering approaches. Engineered scaffolds, and specifically 3D hydrogel-based constructs, have desirable properties, lower cost, higher reproducibility, long-term stability, and they can be rapidly tailored to mimic the native microenvironment and structure. This review explores 3D models in neural tissue engineering, with a particular focus on analysing the benefits and limitations of self-assembled organoids compared with hydrogel-based engineered 3D models. Moreover, this paper will focus on hydrogel based engineered models and probe their biomaterial components, tuneable properties, and fabrication techniques that allow them to mimic native neural tissue structures and environment. Finally, the current challenges and future research prospects of 3D neural models for both self-assembled and engineered models in neural tissue engineering will be discussed.

Analysis of the impact of COVID-19 variants and vaccination on the time-varying reproduction number: statistical methods

Introduction

The COVID-19 pandemic has profoundly impacted global health systems, requiring the monitoring of infection waves and strategies to control transmission. Estimating the time-varying reproduction number is crucial for understanding the epidemic and guiding interventions.

Methods

Probability distributions of serial interval are estimated for Pre-Delta and Delta periods. We conducted a comparative analysis of time-varying reproduction numbers, taking into account population immunity and variant differences. We incorporated the regional heterogeneity and age distribution of the population, as well as the evolving variants and vaccination rates over time. COVID-19 transmission dynamics were analyzed with variants and vaccination.

Results

The reproduction number is computed with and without considering variant-based immunity. In addition, values of reproduction number significantly differed by variants, emphasizing immunity's importance. Enhanced vaccination efforts and stringent control measures were effective in reducing the transmission of the Delta variant. Conversely, Pre-Delta variant appeared less influenced by immunity levels, due to lower vaccination rates. Furthermore, during the Pre-Delta period, there was a significant difference between the region-specific and the non-region-specific reproduction numbers, with particularly distinct pattern differences observed in Gangwon, Gyeongbuk, and Jeju in Korea.

Discussion

This research elucidates the dynamics of COVID-19 transmission concerning the dominance of the Delta variant, the efficacy of vaccinations, and the influence of immunity levels. It highlights the necessity for targeted interventions and extensive vaccination coverage. This study makes a significant contribution to the understanding of disease transmission mechanisms and informs public health strategies.

Spatial variation of methane oxidation and carbon dioxide sequestration in landfill biogeochemical cover

The study investigated the spatial variation of potential methane (CH4) oxidation and residual carbon dioxide (CO2) sequestration in biogeochemical cover (BGCC) system designed to remove CH4, CO2, and hydrogen sulfide (H2S) from landfill gas (LFG) emissions. A 50 cm x 50 cm x 100 cm tank simulated BGCC system, comprising a biochar-amended soil (BAS) layer for CH4 oxidation, a basic oxygen furnace (BOF) slag layer for CO2 and H2S sequestration, and an upper topsoil layer. Synthetic LFG was flushed through the system in five phases, with each corresponding to different compositions and flow rates. Following monitoring, the system was dismantled, and samples were extracted from different depths and locations to analyze spatial variations, focusing on moisture content (MC), organic content (OC), pH, and electrical conductivity (EC). Additionally, batch tests on selected samples from BAS and BOF slag layers were performed to assess potential CH4 oxidation and residual carbonation capacity. The aim of study was to evaluate the BGCC's effectiveness in LFG mitigation, however this study focused on assessing spatial variations in physico-chemical properties, CH4 oxidation in the BAS layer, and residual carbonation in the BOF slag layer. Findings revealed CH4 oxidation in the BAS layer varied between 22.4 and 277.9 µg CH4/g-day, with higher rates in the upper part, and significant spatial variations at 50 cm below ground surface (bgs) compared to 85 cm bgs. The BOF slag layer showed a residual carbonation capacity of 40-49.3 g CO2/kg slag, indicating non-uniform carbonation. Overall, CH4 oxidation and CO2 sequestration capacities varied spatially and with depth in the BGCC system.

Variation and correlation between water retention capacity and gas permeability of compacted loess overburden during wetting-drying cycles

Landfill gases can have numerous detrimental effects on the global climate and urban ecological environment. The protective efficacy of the final cover layer against landfill gases, following exposure to periodic natural meteorological changes during long-term service, remains unclear. This study conducted centrifuge tests and gas permeability tests on compacted loess. The experiments examined the impact and relationship of wetting-drying cycles and dry density on the soil water characteristic curve (SWCC) and gas permeability of compacted loess. Research findings reveal that during the dehumidification process of compacted loess, the gas permeability increases non-linearly, varying the gas permeability of soil with different densities to different extents under wetting-drying cycles. Two models were introduced to describe the impact of wetting-drying cycles on gas permeability of loess with various dry densities, where fitting parameters increased with the number of wetting-drying cycles. Sensitivity analysis of the parameters in the Parker-Van Genuchten-Mualem (P-VG-M) model suggests that parameter γ's accuracy should be ensured in practical applications. Finally, from a microstructural perspective, wetting-drying cycles cause dispersed clay and other binding materials coalesce to fill minuscule pores, leading to an increase in the effective pores responsible for the gas permeability of the soil. These research results offer valuable guidance for designing water retention and gas permeability in compacted loess cover layers under wetting-drying cycles.

Compressibility characteristics of municipal solid waste considering multiple factors

Borehole samples were collected from a municipal solid waste (MSW) landfill in Xi'an, China, and subjected to a series of basic geotechnical and compression tests. This study aims to investigate the influence of composition, dry unit weight, moisture content, organic content, and landfill age on the compressibility of MSW. The results show that with increasing landfill age, the compressible components and organic content exhibit a decreasing trend while the dry unit weight increases. The moisture content does not vary significantly. There is also a linear trend between the logarithm of the primary compression strain and vertical stress. In addition, with an increase in compressible components content, moisture content, and organic content, the modified primary compression index (Cc') shows an increasing trend, whereas with an increase in dry unit weight and landfill age, Cc' shows a decreasing trend. Furthermore, regarding the 34 sets of data, authors only selected five data points for a detailed comparative analysis, this decision was made on the basis that these data points are representative. A modified primary compression index prediction model that considers the dry unit weight, moisture content, and landfill age of the MSW as influencing factors results in a fitting coefficient of 0.797. The Cc' values in this study are within the range of 0.12 to 0.36. These findings provide a reference for the vertical expansion design of existing landfills.

Robot-based 6D bioprinting for soft tissue biomedical applications

Within this interdisciplinary study, we demonstrate the applicability of a 6D printer for soft tissue engineering models. For this purpose, a special plant was constructed, combining the technical requirements for 6D printing with the biological necessities, especially for soft tissue. Therefore, a commercial 6D robot arm was combined with a sterilizable housing (including a high-efficiency particulate air (HEPA) filter and ultraviolet radiation (UVC) lamps) and a custom-made printhead and printbed. Both components allow cooling and heating, which is desirable for working with viable cells. In addition, a spraying unit was installed that allows the distribution of fine droplets of a liquid. Advanced geometries on uneven or angled surfaces can be created with the use of all six axes. Based on often used bioinks in the field of soft tissue engineering (gellan gum, collagen, and gelatin methacryloyl) with very different material properties, we could demonstrate the flexibility of the printing system. Furthermore, cell-containing constructs using primary human adipose-derived stem cells (ASCs) could be produced in an automated manner. In addition to cell survival, the ability to differentiate along the adipogenic lineage could also be demonstrated as a representative of soft tissue engineering.

Amino acid transporters in neurological disorders and neuroprotective effects of cysteine derivatives

For most diseases and disorders occurring in the brain, the full causes behind them are yet unknown, but many show signs of dysfunction of amino acid transporters or abnormalities in amino acid metabolism. The blood-brain barrier (BBB) plays a key role in supporting the function of the central nervous system (CNS). Because of its unique structure, the BBB can maintain the optimal environment for CNS by controlling the passage of hydrophilic molecules from blood to the brain. Nutrients, such as amino acids, can cross the BBB via specific transporters. Many amino acids are essential for CNS function, and dysfunction of these amino acid transporters can lead to abnormalities in amino acid levels. This has been linked to causes behind certain genetic brain diseases, such as schizophrenia, autism spectrum disorder, and Huntington's disease (HD). One example of crucial amino acids is L-Cys, the rate-limiting factor in the biosynthesis of an important antioxidant, glutathione (GSH). Deficiency of L-Cys and GSH has been linked to oxidative stress and has been shown as a plausible cause behind certain CNS diseases, like schizophrenia and HD. This review presents the current status of potential L-Cys therapies and gives future directions that can be taken to improve amino acid transportation related to distinct CNS diseases.

Comparative analysis shows high level of lineage sorting in genomic regions with low recombination in the extended Picea likiangensis species complex

•Phylogenomic analysis uncovers widespread discordance in the extended Picea likiangensis complex.•Introgression (54.99%) and incomplete lineage sorting (ILS; 33.12%) are key drivers of this incongruity.•Recombination rates shape ILS and introgression, with high rates correlating with elevated levels.•Genes linked to abiotic stress responses exhibit significant introgression and ILS, suggesting adaptive evolution.•Lower recombination rates improve accuracy in species relationships.

Detecting and quantifying heterogeneity in susceptibility using contact tracing data

The presence of heterogeneity in susceptibility, differences between hosts in their likelihood of becoming infected, can fundamentally alter disease dynamics and public health responses, for example, by changing the final epidemic size, the duration of an epidemic, and even the vaccination threshold required to achieve herd immunity. Yet, heterogeneity in susceptibility is notoriously difficult to detect and measure, especially early in an epidemic. Here we develop a method that can be used to detect and estimate heterogeneity in susceptibility given contact by using contact tracing data, which are typically collected early in the course of an outbreak. This approach provides the capability, given sufficient data, to estimate and account for the effects of this heterogeneity before they become apparent during an epidemic. It additionally provides the capability to analyze the wealth of contact tracing data available for previous epidemics and estimate heterogeneity in susceptibility for disease systems in which it has never been estimated previously. The premise of our approach is that highly susceptible individuals become infected more often than less susceptible individuals, and so individuals not infected after appearing in contact networks should be less susceptible than average. This change in susceptibility can be detected and quantified when individuals show up in a second contact network after not being infected in the first. To develop our method, we simulated contact tracing data from artificial populations with known levels of heterogeneity in susceptibility according to underlying discrete or continuous distributions of susceptibilities. We analyzed these data to determine the parameter space under which we are able to detect heterogeneity and the accuracy with which we are able to estimate it. We found that our power to detect heterogeneity increases with larger sample sizes, greater heterogeneity, and intermediate fractions of contacts becoming infected in the discrete case or greater fractions of contacts becoming infected in the continuous case. We also found that we are able to reliably estimate heterogeneity and disease dynamics. Ultimately, this means that contact tracing data alone are sufficient to detect and quantify heterogeneity in susceptibility.

Statistical Divergence and Paths Thereof to Socioeconomic Inequality and to Renewal Processes

This paper establishes a general framework for measuring statistical divergence. Namely, with regard to a pair of random variables that share a common range of values: quantifying the distance of the statistical distribution of one random variable from that of the other. The general framework is then applied to the topics of socioeconomic inequality and renewal processes. The general framework and its applications are shown to yield and to relate to the following: f-divergence, Hellinger divergence, Renyi divergence, and Kullback-Leibler divergence (also known as relative entropy); the Lorenz curve and socioeconomic inequality indices; the Gini index and its generalizations; the divergence of renewal processes from the Poisson process; and the divergence of anomalous relaxation from regular relaxation. Presenting a 'fresh' perspective on statistical divergence, this paper offers its readers a simple and transparent construction of statistical-divergence gauges, as well as novel paths that lead from statistical divergence to the aforementioned topics.

A machine learning framework to classify musculoskeletal injury risk groups in military service members

Background

Musculoskeletal injuries (MSKIs) are endemic in military populations. Thus, it is essential to identify and mitigate MSKI risks. Time-to-event machine learning models utilizing self-reported questionnaires or existing data (e.g., electronic health records) may aid in creating efficient risk screening tools.

Methods

A total of 4,222 U.S. Army Service members completed a self-report MSKI risk screen as part of their unit's standard in-processing. Additionally, participants' MSKI and demographic data were abstracted from electronic health record data. Survival machine learning models (Cox proportional hazard regression (COX), COX with splines, conditional inference trees, and random forest) were deployed to develop a predictive model on the training data (75%; n = 2,963) for MSKI risk over varying time horizons (30, 90, 180, and 365 days) and were evaluated on the testing data (25%; n = 987). Probability of predicted risk (0.00-1.00) from the final model stratified Service members into quartiles based on MSKI risk.

Results

The COX model demonstrated the best model performance over the time horizons. The time-dependent area under the curve ranged from 0.73 to 0.70 at 30 and 180 days. The index prediction accuracy (IPA) was 12% better at 180 days than the IPA of the null model (0 variables). Within the COX model, "other" race, more self-reported pain items during the movement screens, female gender, and prior MSKI demonstrated the largest hazard ratios. When predicted probability was binned into quartiles, at 180 days, the highest risk bin had an MSKI incidence rate of 2,130.82 ± 171.15 per 1,000 person-years and incidence rate ratio of 4.74 (95% confidence interval: 3.44, 6.54) compared to the lowest risk bin.

Conclusion

Self-reported questionnaires and existing data can be used to create a machine learning algorithm to identify Service members' MSKI risk profiles. Further research should develop more granular Service member-specific MSKI screening tools and create MSKI risk mitigation strategies based on these screenings.

The Transformative Power of Virtual Hospitals for Revolutionising Healthcare Delivery

Objectives: The objective of this narrative review is to explore the advantages and limitations of VHs in delivering healthcare, including access to specialized professionals, streamlined communication, efficient scheduling, integration of electronic health records, ongoing monitoring, and support, transcending geographical boundaries, and resource optimization. Methods: Review of literature. Results: The national healthcare systems are facing an alarming rise in pressure due to global shifts. Virtual hospitals (VH) offer a practical solution to numerous systemic challenges, including rising costs and increased workloads for healthcare providers. VH also facilitate the delivery of personalized services and enable the monitoring of patients beyond the conventional confines of healthcare settings, reducing the reliance on waiting medicine carried out in doctors' offices or hospitals. Conclusion: VH can mirror the conventional healthcare referral system.

Disinterested attention and aesthetic experience

Research studies have focused on stimulus features as well as internal or contextual factors to understand aesthetic experience. An important question is the nature of processes that are involved in all aesthetic experiences. One possible process is "disinterested attention" that may be necessary for one to have an aesthetic experience. This can be contrasted with a perceiver who attends to an object or event only in a goal-directed or instrumental or practical manner. It has been claimed that "disinterested attention" involves attention being focused on the aesthetic object or event while being distributed across its features or components. Other ideas have focused on better reallocation of attention over time. The potential nature of attention could be linked to aspects of mindfulness. Studies looking at the effects of mindfulness on aesthetic experience have shown it increases the frequency of having aesthetic experience. The nature of attention needed for an aesthetic experience can be thought of as a form of generosity that could be linked to the notions of a gift. Mindful attention to objects or life as a gift, perhaps enables us to see objects and perhaps life itself in non-instrumental terms resulting in an aesthetic experience.

Determination of emission factors from a landfill through an inverse methodology: Experimental determination of ambient air concentrations and use of numerical modelling

The application of numeric modelling for determining the impact of landfills needs for reliable emission source data. In this study, a methodology for the characterization of the emission profiles of the different sources present in landfills for emission factors determination, applying an indirect methodology, is presented. Ambient air concentrations of volatile organic compounds (VOCs), hydrogen sulphide (H2S) and ammonia (NH3) were determined in three potentially emission sources in Can Mata landfill (Hostalets de Pierola, Catalonia, Spain): dumping areas, pre-closed zone and leachate reservoir as well as in biogas, for the determination of emission factors. Multi-sorbent bed and Tenax TA tubes were used for a wide range of VOCs sampling, and analysis was conducted through TD-GC/MS. H2S and NH3 were sampled and analysed using Radiello passive samplers. The highest total VOC (TVOC) concentrations were found in dumping areas (0.7-3.5 mg m-3), followed by leachate reservoir (0.3-0.6 mg m-3) and pre-closed area (77-165 μg m-3). On the other hand, the highest H2S and NH3 concentrations were found in leachate reservoir, presenting values of 0.8-1.1 mg m-3 and 1.7-1.8 mg m-3, respectively. With the application of odour thresholds to the concentrations obtained, the most critical compounds regarding odour annoyances were determined. The highest odour units (O.U.) were found in leachate reservoir due to H2S concentrations, whereas VOCs contributed mainly to O.U. in the dumping areas. The obtained ambient air concentrations were used for the indirect determination of the emission factors through numerical modelling using a Eulerian dispersion model. The emission factors obtained for the landfill for TVOC, H2S and NH3 were in the range of 0.44-10.9 g s-1, 0.16-1.02 g s-1 and 0.23-1.82 g s-1, respectively, depending on the emission source. Reliable emission factors are crucial to obtain landfill impact maps, which are essential for the correct management of these facilities.

Nanowires: Exponential speedup in quantum computing

This review paper examines the crucial role of nanowires in the field of quantum computing, highlighting their importance as versatile platforms for qubits and vital building blocks for creating fault-tolerant and scalable quantum information processing systems. Researchers are studying many categories of nanowires, including semiconductor, superconducting, and topological nanowires, to explore their distinct quantum features that play a role in creating various qubit designs. The paper explores the interdisciplinary character of quantum computing, combining the fields of quantum physics and materials science. This text highlights the significance of quantum gate operations in manipulating qubits for computation, thus creating the toolbox of quantum algorithms. The paper emphasizes the key research areas in quantum technology, such as entanglement engineering, quantum error correction, and a wide range of applications spanning from encryption to climate change modeling. The research highlights the importance of tackling difficulties related to decoding mitigation, error correction, and hardware scalability to fully utilize the transformative potential of quantum computing in scientific, technical, and computational fields.

Modulation of the strength of weak S-H⋯O hydrogen-bond: Spectroscopic study of the complexes of 2-flurothiophenol with methanol and ethanol

Spectroscopic exploration of sulfur-centered hydrogen bonding involving a thiol group (S-H) as the hydrogen bond donor is scarce in the literature. Herein, we have investigated 1:1 complexes of 2-fluorothiophenol (2-FTP) with methanol (MeOH) and ethanol (EtOH) in the gas phase to examine the physical characteristics and strength of the S-H⋯O hydrogen bond. Structures, conformations, and the strength of the S-H⋯O interaction are investigated by measuring the electronic and Infrared (IR) spectra of the two complexes employing resonant two-photon ionization, UV-UV hole-burning, and IR-UV double resonance spectroscopic techniques combined with quantum chemical calculations. Three conformers of 2-FTP⋯MeOH and two conformers of 2-FTP⋯EtOH have been detected in the experiment. A comparison of the IR spectra obtained from the experiment with those of the low-energy conformers of 2-FTP⋯MeOH and 2-FTP⋯EtOH predicted from the theory confirms that all the observed conformers of the two complexes are primarily S-H⋯O hydrogen bonded. The IR red-shifts found in the S-H stretching frequencies in 2-FTP⋯MeOH and 2-FTP⋯EtOH concerning that in 2-FTP are ∼76 and ∼88 cm-1, respectively, which are much larger than that was reported earlier in the 2-FTP⋯H2O complex (30 cm-1). The strength and physical nature of different noncovalent interactions, including the S-H⋯O hydrogen bond existing in the complexes, are further analyzed using natural bond orbital analysis, quantum theory of atoms in molecules, and localized molecular orbital-energy decomposition analysis. The current investigation reveals that the S-H⋯O hydrogen bond can be strengthened by judicial choices of the hydrogen bond acceptors of higher proton affinities.

Structural Elucidation of N2O Clusters at Low Temperatures: Exemplary Framework Stabilized by π-Hole-Driven N···O and N···N Pnicogen Bonding Interactions

N2O is a classic prototype, in which central nitrogen is sufficiently electropositive with a positive potential of 20 kcal mol-1 in magnitude to qualify it as a possible pnicogen. This was applied to a test with N2O clusters using ab initio calculations in association with various molecular topographic tools. The structure of the energetically dominant and N2O dimer was in favor of a perpendicular geometry, where the central nitrogen atom of the N2O submolecule assumed a near 90° angle with the adjacent N═O and/or N═N moiety, which provides the affirmation of central nitrogen as a possible π-hole-driven pnicogen. The terminal nitrogen and oxygen atoms of N2O continue to act as conventional electron donors (Lewis bases) with a negative potential. Overall, predominant π-hole-driven N···O and N···N pnicogen bonding interactions were observed to stabilize N2O clusters. Furthermore, N2O clusters (dimers and trimers) were synthesized at low temperatures in an Ar matrix using molecular beam (effusive and supersonic expansion) experiments. The geometries of these clusters were characterized by probing infrared spectroscopy with corroboration from ab initio computational methods. In addition to our previously investigated nitromethane and nitrobenzene systems, N2O also makes it to a pnicogen bonder's club with the central nitrogen as a π-hole-driven pnicogen.

Cooperativity between Intermolecular Hydrogen and Carbon Bonds in ZY···CH3CN/CH3NC···HX Trimers (ZY = H2O, H2S, HF, HCl, HBr, NH3, and H2CO; HX = HF, HCl, and HBr)

Hydrogen-bonding and carbon-bonding interactions are widespread in nature. We studied the cooperativity between these interactions in 42 trimeric complexes ZY···CH3CN/CH3NC···HX, where ZY molecules are H2O, H2S, HF, HCl, HBr, NH3, and H2CO, and HX molecules are HF, HCl, and HBr. Acetonitrile (CH3CN) and isoacetonitrile (CH3NC) act as hydrogen bond acceptors as well as carbon bond donors in these trimers. Various theoretical methods, such as electronic structure calculations, quantum theory of atoms in molecule (QTAIM), natural bond orbital (NBO), and reduced density gradient analysis, are employed to study these trimers, and the results are compared with the corresponding ZY···CH3CN/CH3NC and CH3CN/CH3NC···HX dimers. Electronic structure calculations are performed at the second-order Mo̷ller-Plesset perturbation theory using the 6-311++G(2d,2p) basis set. We show that both the interactions act synergistically in these trimers leading to an increase in their bond strength as compared to the strength in the individual dimers. The cooperative energies for these trimers are in the range of 0.69 to 3.22 kJ/mol. It is seen that the carbon bonds benefit more from the cooperativity than the hydrogen bonds. The trends of cooperativity and correlations of interaction energies and cooperative energies with relevant QTAIM and NBO parameters are reported.

Parametric analysis of SARS-CoV-2 dose-response models in transportation scenarios

Transportation systems involve high-density crowds of geographically diverse people with variations in susceptibility; therefore, they play a large role in the spread of infectious diseases like SARS-CoV-2. Dose-response models are widely used to model the relationship between the trigger of a disease and the level of exposure in transmission scenarios. In this study, we quantified and bounded viral exposure-related parameters using empirical data from five transportation-related events of SARS-CoV-2 transmission. Dose-response models were then applied to parametrically analyze the infection spread in generic transportation systems, including a single-aisle airplane, bus, and railway coach, and then examined the mitigating efficiency of masks by performing a sensitivity analysis of the related factors. We found that dose level significantly affected the number of secondary infections. In general, we observed that mask usage reduced infection rates at all dose levels and that high-quality masks equivalent to FFP2/N95 masks are effective for all dose levels. In comparison, we found that lower-quality masks exhibit limited mitigation efficiency, especially in the presence of high dosage. The sensitivity analysis indicated that a reduction in the infection distance threshold is a critical factor in mask usage.

Impact assessment of self-medication on COVID-19 prevalence in Gauteng, South Africa, using an age-structured disease transmission modelling framework

OBJECTIVE

To assess the impact of self-medication on the transmission dynamics of COVID-19 across different age groups, examine the interplay of vaccination and self-medication in disease spread, and identify the age group most prone to self-medication.

METHODS

We developed an age-structured compartmentalized epidemiological model to track the early dynamics of COVID-19. Age-structured data from the Government of Gauteng, encompassing the reported cumulative number of cases and daily confirmed cases, were used to calibrate the model through a Markov Chain Monte Carlo (MCMC) framework. Subsequently, uncertainty and sensitivity analyses were conducted on the model parameters.

RESULTS

We found that self-medication is predominant among the age group 15-64 (74.52%), followed by the age group 0-14 (34.02%), and then the age group 65+ (11.41%). The mean values of the basic reproduction number, the size of the first epidemic peak (the highest magnitude of the disease), and the time of the first epidemic peak (when the first highest magnitude occurs) are 4.16499, 241,715 cases, and 190.376 days, respectively. Moreover, we observed that self-medication among individuals aged 15-64 results in the highest spreading rate of COVID-19 at the onset of the outbreak and has the greatest impact on the first epidemic peak and its timing.

CONCLUSION

Studies aiming to understand the dynamics of diseases in areas prone to self-medication should account for this practice. There is a need for a campaign against COVID-19-related self-medication, specifically targeting the active population (ages 15-64).