Enhancing vaccination strategies for epidemic control through effective lockdown measures.

Evolutionary epidemiology models have substantially impacted the study of various infections and prevention methods in the biology field. These models are called Susceptible, Lockdown, Vaccinated, Infected, and Recovered (SLVIR) epidemic dynamics. We explore how human behavior, particularly in the context of disease transmission, is influenced by two intervention strategies: vaccination and lockdown, both of which are grounded in the principles of evolutionary game theory (EGT). This comprehensive study using evolutionary game theory delves into the dynamics of epidemics, explicitly focusing on the transition rate from susceptibility to immunity and susceptibility to lockdown measures. Our research involves a thorough analysis of the structural aspects of the SLVIR epidemic model, which delineates disease-free equilibria to ensure stability in the system. Our investigation supports the notion that implementing lockdown measures effectively reduces the required level of vaccinations to curtail the prevalence of new infections. Furthermore, it highlights that combining both strategies is particularly potent when an epidemic spreads rapidly. In regions where the disease spreads comparatively more, our research demonstrates that lockdown measures are more effective in reducing the spread of the disease than relying solely on vaccines. Through significant numerical simulations, our research illustrates that integrating lockdown measures and efficient vaccination strategies can indirectly lower the risk of infection within the population, provided they are both dependable and affordable. The outcomes reveal a nuanced and beneficial scenario where we examine the interplay between the evolution of vaccination strategies and lockdown measures, assessing their coexistence through indicators of average social payoff.

Analyzing evolutionary game theory in epidemic management: A study on social distancing and mask-wearing strategies.

When combating a respiratory disease outbreak, the effectiveness of protective measures hinges on spontaneous shifts in human behavior driven by risk perception and careful cost-benefit analysis. In this study, a novel concept has been introduced, integrating social distancing and mask-wearing strategies into a unified framework that combines evolutionary game theory with an extended classical epidemic model. To yield deeper insights into human decision-making during COVID-19, we integrate both the prevalent dilemma faced at the epidemic's onset regarding mask-wearing and social distancing practices, along with a comprehensive cost-benefit analysis. We explore the often-overlooked aspect of effective mask adoption among undetected infectious individuals to evaluate the significance of source control. Both undetected and detected infectious individuals can significantly reduce the risk of infection for non-masked individuals by wearing effective facemasks. When the economical burden of mask usage becomes unsustainable in the community, promoting affordable and safe social distancing becomes vital in slowing the epidemic's progress, allowing crucial time for public health preparedness. In contrast, as the indirect expenses associated with safe social distancing escalate, affordable and effective facemask usage could be a feasible option. In our analysis, it was observed that during periods of heightened infection risk, there is a noticeable surge in public interest and dedication to complying with social distancing measures. However, its impact diminishes beyond a certain disease transmission threshold, as this strategy cannot completely eliminate the disease burden in the community. Maximum public compliance with social distancing and mask-wearing strategies can be achieved when they are affordable for the community. While implementing both strategies together could ultimately reduce the epidemic's effective reproduction number ([Formula: see text]) to below one, countries still have the flexibility to prioritize either of them, easing strictness on the other based on their socio-economic conditions.

Assessing the instantaneous social dilemma on social distancing attitudes and vaccine behavior in disease control.

In the face of infectious disease outbreaks, the collective behavior of a society can has a profound impact on the course of the epidemic. This study investigates the instantaneous social dilemma presented by individuals' attitudes toward vaccine behavior and its influence on social distancing as a critical component in disease control strategies. The research employs a multifaceted approach, combining modeling techniques and simulation to comprehensively assess the dynamics between social distancing attitudes and vaccine uptake during disease outbreaks. With respect to modeling, we introduce a new vaccination game (VG) where, unlike conventional VG models, a 2-player and 2-strategy payoff structure is aptly embedded in the individual behavior dynamics. Individuals' willingness to adhere to social distancing measures, such as mask-wearing and physical distancing, is strongly associated with their inclination to receive vaccines. The study reveals that a positive attitude towards social distancing tends to align with a higher likelihood of vaccine acceptance, ultimately contributing to more effective disease control. As the COVID-19 pandemic has demonstrated, swift and coordinated public health measures are essential to curbing the spread of infectious diseases. This study underscores the urgency of addressing the instantaneous social dilemma posed by individuals' attitudes. By understanding the intricate relationship between these factors, policymakers, and healthcare professionals can develop tailored strategies to promote both social distancing compliance and vaccine acceptance, thereby enhancing our ability to control and mitigate the impact of disease outbreaks in the future.

Behavioral game of quarantine during the monkeypox epidemic: Analysis of deterministic and fractional order approach.

This work concerns the epidemiology of infectious diseases like monkeypox (mpox) in humans and animals. Our models examine transmission scenarios, including transmission dynamics between humans, animals, and both. We approach this using evolutionary game theory, specifically the intervention game-theoretical (IGT) framework, to study how human behavior can mitigate disease transmission without perfect vaccines and treatments. To do this, we use non-pharmaceutical intervention, namely the quarantine policy, which demonstrates the delayed effect of the epidemic. Additionally, we contemplate quarantine-based behavioral intervention policies in deterministic and fractional-order models to show behavioral impact in the context of the memory effect. Firstly, we extensively analyzed the model's positivity and boundness of the solution, reproduction number, disease-free and endemic equilibrium, possible stability, existence, concavity, and Ulam-Hyers stability for the fractional order. Subsequently, we proceeded to present a numerical analysis that effectively illustrates the repercussions of varying quarantine-related factors, information probability, and protection probability. We aimed to comprehensively examine the effects of non-pharmaceutical interventions on disease control, which we conveyed through line graphs and 2D heat maps. Our findings underscored the significant influence of strict quarantine measures and the protection of both humans and animals in mitigating disease outbreaks. These measures not only significantly curtailed the spread of the disease but also delayed the occurrence of the epidemic's peak. Conversely, when quarantine maintenance policies were implemented at lower rates and protection levels diminished, we observed contrasting outcomes that exacerbated the situation. Eventually, our analysis revealed the emergence of animal reservoirs in cases involving disease transmission between humans and animals.

Understanding the relationship between stay-at-home measures and vaccine shortages: a conventional, heterogeneous, and fractional dynamic approach.

In light of the global prevalence of a highly contagious respiratory disease, this study presents a novel approach to address the pressing and unanticipated issues by introducing a modified vaccination and lockdown-centered epidemic model. The rapid spread of the disease is attributed to viral transmissibility, the emergence of new strains (variants), lack of immunization, and human unawareness. This study aims to provide policymakers with crucial insights for making informed decisions regarding lockdown strategies, vaccine availability, and other control measures. The research adopts three types of models: deterministic, heterogeneous, and fractional-order dynamics, on both theoretical and numerical approaches. The heterogeneous network considers varying connectivity and interaction patterns among individuals, while the ABC fractional-order derivatives analyze the impact of integer-order control in different semi-groups. An extensive theoretical analysis is conducted to validate the proposed model. A comprehensive numerical investigation encompasses deterministic, stochastic, and ABC fractional-order derivatives, considering the combined effects of an effective vaccination program and non-pharmaceutical interventions, such as lockdowns and shutdowns. The findings of this research are expected to be valuable for policymakers in different countries, helping them implement dynamic strategies to control and eradicate the epidemic effectively.

A non-singular fractional-order logistic growth model with multi-scaling effects to analyze and forecast population growth in Bangladesh.

This paper is primarily concerned with data analysis employing the nonlinear least squares curve fitting method and the mathematical prediction of future population growth in Bangladesh. Available actual and adjusted census data (1974-2022) of the Bangladesh population were applied in the well-known autonomous logistic population growth model and found that all data sets of the logistic (exact), Atangana-Baleanu-Caputo (ABC) fractional-order derivative approach, and logistic multi-scaling approximation fit with good agreement. Again, the existence and uniqueness of the solution for fractional-order and Hyers-Ulam stability have been studied. Generally, the growth rate and maximum environmental support of the population of any country slowly fluctuate with time. Including an approximate closed-form solution in this analysis confers several advantages in assessing population models for single species. Prior studies predominantly employed constant growth rates and carrying capacity, neglecting the investigation of fractional-order methods. Thus, the current study fills a crucial gap in the literature by introducing a more formal approach to analyzing population dynamics. Therefore, we bank on the findings of this article to contribute to accurate population forecasting and planning, national development, and national progress.

Psychological Distress among Health Professional Students during Covid-19 Pandemic.

The Corona virus pandemic has arisen as a truly intimidating threat to the very existence of humankind. Isolation, contact boundaries and economic blackout impose a complete transformation to the psychosocial environment in affected countries. The psychological impairments are significant to the diagnosed cases, suspected cases, quarantine cases, health workers and their families and also relatives. In this unprecedented global crisis situation created by Covid-19, it is very important and critical to recognize the mental shock that can affect people in different ways around the globe. The purpose of this study was to evaluate the levels of Psychological distress among Health Professional students in Bangladesh. Data collection was done through online questionnaire. Participants were asked to complete the socio-demographic data, the 6 item Kessler Psychological Distress Scale (K6), acute stress reaction (ASR) using the Impact of Event Scale-Revised (IES-R) scale and any history of bitter experience during childhood, previous psychological illness, internet addiction and relationship with family. By using logistic regression, we examined the associations of the above variables with psychological distress and ASR. Among the 281 respondents, 97(34.52%) students reported high psychological distress, while 123(43.77%) had the criteria for acute stress reaction in response to the Covid-19 outbreak. A total of 39(13.87%) students showed signs of both distress and acute stress reaction. An increased psychological distress was found with previous psychological illness (OR 1.378, 95% CI 0.719-2.642). In contrast, good family functioning was associated with decreased risks of distress (OR 0.385, 95% CI 0.242-0.613) and acute stress reaction (OR 0.812, 95% CI 0.529-1.249). The study result shows that the group which is mostly affected psychologically after the corona virus pandemic are women. Almost one-third respondents had a significant psychological impact. This result suggests us further systematic and longitudinal assessment of psychological needs among the populations. Therefore, in further psychiatric planning priority should be there for this finding.

Impact of social-functioning and sleep on quality of life in chronic inflammatory demyelinating polyneuropathy.

INTRODUCTION/AIMS: The impact of impairment of social functioning and sleep on health-related quality of life (HR-QoL), is unknown in chronic inflammatory demyelinating polyneuropathy (CIDP). The value of the Chronic Acquired Polyneuropathy Patient-Reported Index (CAP-PRI) to identify potential social functioning and sleep issues is equally unknown. METHODS: We performed a cross-sectional evaluation of social functioning and sleep using the "Scales for Outcomes in Parkinson's Disease" (SCOPA) in 40 subjects with clinically-stable CIDP through a structured questionnaire. We assessed HR-QoL through the CAP-PRI. Disability was evaluated through the Overall Neuropathy Limitation Score (ONLS). RESULTS: SCOPA social functioning scores were impaired at least "a little" per averaged item in > 50 % of subjects, and at least "quite a bit" per averaged item in > 20 %. Most affected items were (i) difficulty with work/household/other chores (ii) difficulties with hobbies/sport/leisure activities. SCOPA sleep sub-scores indicated at least "a little concern" for night-time sleep in nearly 50 % of subjects. Abnormal sleep timing was rare. Associations were found between both SCOPA social-functioning and SCOPA sleep scores and the CAP-PRI. Linear regression demonstrated the SCOPA social-functioning score was independently associated with the CAP-PRI. The CAP-PRI showed high association with disability scores, good internal consistency, absence of ceiling effect, absence of significant floor-effect, and good criteria-related as well as construct-related validity. DISCUSSION: Social functioning and night-time sleep are frequently affected in CIDP and impact on HR-QoL. In contrast to traditional disability scales, the CAP-PRI additionally allows adequately capturing these impairments and may represent an adequate holistic outcome measure.

Behavioral vaccination policies and game-environment feedback in epidemic dynamics.

Many policymakers have adopted voluntary vaccination policies to alleviate the consequences of contagious diseases. Such policies have several well-established feathers, i.e. they are seasonal, depending on an individual's decision, adaptive, and control epidemic activity. Here, we study ideas from behavioral epidemiology embedded with a vaccination game and pairwise two-player two-strategy game to represent the environmental feedback in an SVIR model by using a composite information index including disease incidence, vaccine factors and cooperative behavior on a global time scale (repeated season). In its turn, the information index's game dynamics to participate in the vaccine program (cooperation) is supposed to reflect the feedback-evolving dynamics of competitive cognitions and the environment. The assuming model is described by two different evolutionary game systems connected by an unknown external public opinion environment feedback. The embedded model is described by an inherited system showing a behavioral aspect, i.e. pairwise game indicates an individual's cooperative behavior, and a vaccine game refers to vaccine-cost influence. This is a novel attempt to stabilize the two different decision processes to pool them into a single index. Extensive simulations suggest a rich spectrum of achievable results, including epidemic control, human behavior, social dilemma, and policy suggestions.

Understanding the Impact of Vaccination and Self-Defense Measures on Epidemic Dynamics Using an Embedded Optimization and Evolutionary Game Theory Methodology.

Explaining how individual choice and government policy can appear in the same context in real society is one of the most challenging scientific problems. Controlling infectious diseases requires effective prevention and control measures, including vaccination and self-defense measures. In this context, optimal control strategies incorporating vaccination and self-defense measures have been proposed using the framework of evolutionary game theory. This approach accounts for individuals' behavior and interactions in a population. It can provide insights into the effectiveness of different strategies for controlling the spread of infectious diseases. The optimal control strategy involves balancing the costs and benefits of vaccination, considering the dynamic interplay between the infected and susceptible populations. By combining evolutionary game theory with optimal control theory, we can identify the optimal allocation of resources for vaccination and self-defense measures, which can maximize the control of infectious diseases while minimizing costs. The model is utilized to analyze public health policies diseases, such as vaccination and self-defense strategies, to mitigate the spread of infectious in the context of delayed decision-making.

Analyzing the Costs and Benefits of Utilizing a Mixed-Strategy Approach in Infectious Disease Control under a Voluntary Vaccination Policy.

Infectious diseases pose significant public health risks, necessitating effective control strategies. One such strategy is implementing a voluntary vaccination policy, which grants individuals the autonomy to make their own decisions regarding vaccination. However, exploring different approaches to optimize disease control outcomes is imperative, and involves assessing their associated costs and benefits. This study analyzes the advantages and disadvantages of employing a mixed-strategy approach under a voluntary vaccination policy in infectious disease control. We examine the potential benefits of such an approach by utilizing a vaccination game model that incorporates cost and benefit factors, where lower costs and higher benefits lead to reduced infection rates. Here, we introduce a mixed-strategy framework that combines individual-based risk assessment (IB-RA) and society-based risk assessment (SB-RA) strategies. A novel dynamical equation is proposed that captures the decision-making process of individuals as they choose their strategy based on personal or communal considerations. In addition, we explore the implications of the mixed-strategy approach within the context of social dilemmas. We examine deviations from expected behavior and the concept of social efficiency deficit (SED) by allowing for the evolution of vaccine strategy preferences alongside risk perception. By comprehensively evaluating the financial implications and societal advantages associated with the mixed-strategy approach, decision-makers can allocate resources and implement measures to combat infectious diseases within the framework of a voluntary vaccination policy.

Exploring the performance of volatile mutations on evolutionary game dynamics in complex networks.

The typical framework of replicator dynamics in evolutionary game theory assumes that all mutations are equally likely, meaning that the mutation of an evolving inhabitant only contributes constantly. However, in natural systems in biological and social sciences, mutations can arise due to their repetitive regeneration. The phenomenon of changing strategies (updating), typically prolonged sequences repeated many times, is defined as a volatile mutation that has been overlooked in evolutionary game theory. Implementing a repeated time framework introduces a dynamic mutation aspect incorporated with the pairwise Fermi rule. Network structure, ubiquitous in many natural and artificial systems, has significantly affected the dynamics and outcomes of evolutionary games. We examine the evolution of the pairwise game in terms of dilemma strength. It is revealed that mutation intensity can influence evolutionary dynamics. We also demonstrated that the obtained outcomes run by the deterministic and multi-agent simulation (MAS) process present similar stability regions for both linear and non-linear dynamics, even in various game classes. In particular, the most stimulating effect is detected for the relationship between the fraction of cooperation and the fraction of the mutated individuals, as inclination tends to provide an increasing tendency and supporting defection in the opposite case. In conclusion, we identified a form of volatile mutation as a form of noise that, under certain situations, could be used to enhance cooperation in social systems and design strategies for promoting cooperation in networked environments.

Impact of human cooperation on vaccination behaviors.

This paper studies a dynamic vaccination game model embedded with vaccine cost-effectiveness and dyadic game during an epidemic, assuming the appearance of cooperation among individuals from an evolutionary perspective. The infection dynamics of the individuals' states follow a modified S/VIS (susceptible/vaccinated-infected-susceptible) dynamics. Initially, we assume that the individuals are unsure about their infection status. Thus, they make decisions regarding their options based on their neighbors' perceptions, the prevalence of the disease, and the characteristics of the available vaccines. We then consider the strategy updating process IBRA (individuals-based risk assessment) concerning an individual's committing vaccination based on a neighbor's decision. In the perspective of social dilemma, it presents the idea of social efficiency deficit to find the gap between social optimum and Nash equilibrium point based on dilemma strength by considering vaccine decision. The cost and cooperative behavior depend on disease severity, neighbor's attitude, and vaccine properties to obtain a reduced-order optimal solution to control infectious diseases. Vaccine factors (efficiency, cost, and benefit) are crucial in changing human vaccine decisions and cooperative behavior. It turns out that, even in the prisoner's dilemma case, where all defection attitude occurs, vaccine uptake (cooperation) increases. Finally, extensive numerical studies were presented that illustrate interesting phenomena and investigate the ultimate extent of the epidemic, vaccination coverage, average social benefits, and the social efficiency deficit concerning optimal strategies and the dynamic vaccine attitudes of individuals. PACS numbers. Theory and modeling; computer simulation, 87.15. Aa; Dynamics of evolution, 87.23. Kg.

A cyclic behavioral modeling aspect to understand the effects of vaccination and treatment on epidemic transmission dynamics.

Evolutionary epidemiological models have played an active part in analyzing various contagious diseases and intervention policies in the biological sciences. The design in this effort is the addition of compartments for treatment and vaccination, so the system is designated as susceptible, vaccinated, infected, treated, and recovered (SVITR) epidemic dynamic. The contact of a susceptible individual with a vaccinated or an infected individual makes the individual either immunized or infected. Inventively, the assumption that infected individuals enter the treatment and recover state at different rates after a time interval is also deliberated through the presence of behavioral aspects. The rate of change from susceptible to vaccinated and infected to treatment is studied in a comprehensive evolutionary game theory with a cyclic epidemic model. We theoretically investigate the cyclic SVITR epidemic model framework for disease-free and endemic equilibrium to show stable conditions. Then, the embedded vaccination and treatment strategies are present using extensive evolutionary game theory aspects among the individuals in society through a ridiculous phase diagram. Extensive numerical simulation suggests that effective vaccination and treatment may implicitly reduce the community risk of infection when reliable and cheap. The results exhibited the dilemma and benefitted situation, in which the interplay between vaccination and treatment evolution and coexistence are investigated by the indicators of social efficiency deficit and socially benefited individuals.

Coupled simultaneous analysis of vaccine and self-awareness strategies on evolutionary dilemma aspect with various immunity.

On evolutionary game theory (EGT), two intervention policies: vaccination and self-awareness, are considered to account for how human attitude impacts disease spreading. Although these interventions can impose, their implementation may depend on the various immunity systems such as shield immunity, innate immunity, waning immunity, natural immunity, and artificial immunity. This framework provides an epidemic SEIRVA (susceptible-exposed-infected-removed-vaccinated-aware) model and two EGT dynamics to analyze the interplay between the immunity system and social learning interventions. The prospect of exploring the individual's strategy and social dilemma for removing a disease could assist design an effective vaccine program and self-awareness policy. Also, we evaluated the indicator of social efficiency deficit (SED) for a social dual-dilemma to measure the presence of a dilemma situation. Extensive theoretical analysis displays that stability includes the reproduction number, conditions for positivity and uniqueness, and the strength number analyzed in the equilibria, including fundamental properties validated by numerical simulation of the discretization method that appraises a variety of graphs at adjusting parameters. We present extensive numerical studies investigating the affect of controlling parameters, individual vulnerability, optimal policies, and individual costs. It turns out that, even with the affordable vaccine, individuals may have very different behaviors; self-awareness strategy plays a vital role in controlling diseases.

Social distancing as a public-good dilemma for socio-economic cost: An evolutionary game approach.

Partaking in social distancing can contribute to a public good affected by the perceived risk of infection and socioeconomic cost. Although social distancing can save lives by slowing down the disease transmission before introducing any effective medical intervention, the economic fallout of social distancing can be brutal for the poorest, vulnerable, and marginalized members of society. We combined the epidemiological and evolutionary game theoretical (EGT) framework through the consolidations of the SEIR (Susceptible-Exposed-Infected-Removed) disease model to analyze behavior enticements in a social distancing dilemma situation with the complex behavioral decision-making aspect. Extensive theoretical and numerical analyses reveal that socioeconomic cost and infected individuals' compliance behavior are critical factors in reining disease spread in the community. Lower cost for maintaining relative safety distance encourages maximum avoidance of public interactions by a detected infected individual. The benefitted fraction due to compliance is parted from the naturally immunized population. People get insignificant benefits from social distancing when the disease transmission rate is too low or crosses critical higher values. Average Social Payoff (ASP) analysis suggests the correspondence of significant safety distance with lowest cost setting as the best strategy to derive the maximum goods. But mounting inherent cost converts social distancing obedience to a public good dilemma.

Separation of disaccharide epimers, anomers and connectivity isomers by high resolution differential ion mobility mass spectrometry.

Glycans are ubiquitous, structurally diverse molecules that have specific and general roles involving metabolism, structure, and cell-to-cell signaling. Functional specificity depends strongly on the complexity of structures that polysaccharides can adopt based on their subunit composition, length, extent of branching, glycosidic bond connectivity and anomeric configuration. However, a rapid and comprehensive characterization of glycan isomers can be challenging owing to limitations associated with their separation. Here, ten composition, anomeric and connectivity disaccharide isomers were separated and detected using high-resolution differential ion mobility-mass spectrometry (DMS-MS, also known as FAIMS). Focus was primarily directed to compositional isomers corresponding to epimers that differ by the axial or equatorial position of a single hydroxyl group. DMS resolving power was enhanced 14-fold primarily by increasing the fraction of helium in the ion carrier gas and lowering the flow rate. At relatively high disaccharide concentrations, DMS-MS of each disaccharide resulted in complex and unique multi-peak spectra with up to ten fully and partially resolved peaks for ß-1,4-mannobiose (Man-1,4ß-Man), which can be attributed to the DMS separation and subsequent dissociation of ionic non-covalently bound oligomers into monomer ions. Each DMS spectrum has at least one differentiating peak that is not in the other spectra, indicating that DMS can be used to fully or partially resolve composition, configuration and connectivity isomers. At relatively low disaccharide concentrations, mixtures of disaccharide epimers can also be readily separated by DMS. The integration of high-resolution, ambient pressure DMS with complementary reduced-pressure ion mobility and MS-based glycomics and glycoproteomics workflows may be useful for improving the characterization of glycans and glycosylated biomolecules.

An atmospheric pressure ion funnel with a slit entrance for enhancing signal and resolution in high resolution differential ion mobility mass spectrometry.

Differential ion mobility (DMS) is a versatile ion separation method that is often integrated with mass spectrometry (MS). In DMS, extremely high electric fields are used such that ion mobility depends non-linearly on electric field and thus, ion separations can be more orthogonal to MS than lower field ion mobility-based methods. DMS can have sufficiently high resolution to be used for enantiomer analysis of small molecules and to separate protein ions with peak widths comparable to those obtained for peptides. However, the performance of high resolution DMS-MS can be limited owing to the substantial loss of ions (>10-fold) that can occur upon their transfer from atmospheric pressure (where DMS separation typically occurs) to vacuum through a narrow conductance limited inlet (e.g. capillary) to the MS. Here, results from simulated ion trajectory simulations suggest that in high resolution DMS most ions can be lost by 'crashing' onto the narrow capillary inlet after exiting the DMS separation channel. To enhance DMS sensitivity and resolving power, an integrated DMS-MS interface concept is reported that consists of a slit electrode and a 12-electrode atmospheric pressure ion funnel (APIF). By using an APIF with slit entrance, the simulated ion transmission efficiencies increase by up to 257% for singly charged ions ([DMMP + H]+, [tryptophan + H]+, and [(2-dodecanone)2 + H]+) and by 209% for [ubiquitin + 12H]12+, without compromising resolving power. The use of APIF improves the ion focussing from the DMS exit to the MS capillary to improve sensitivity, and the slit ensures that ion dispersion in the analytically relevant direction perpendicular to the DMS electrodes is restricted to enhance resolution. By narrowing the slit of the DMS-Slit-APIF interface, the DMS resolving power can be increased further by at least 20%. Overall, these results indicate that the integrated DMS-Slit-APIF interface is promising for improving the sensitivity and resolution for many different types of DMS-MS experiments.

Modeling the epidemic control measures in overcoming COVID-19 outbreaks: A fractional-order derivative approach.

Novel coronavirus named SARS-CoV-2 is one of the global threads and uncertain challenges worldwide faced at present. It has stroke rapidly around the globe due to viral transmissibility, new variants (strains), and human unconsciousness. Lack of adequate and reliable vaccination and proper treatment, control measures such as self-protection, physical distancing, lockdown, quarantine, and isolation policy plays an essential role in controlling and reducing the pandemic. Decisions on enforcing various control measures should be determined based on a theoretical framework and real-data evidence. We deliberate a general mathematical control measures epidemic model consisting of lockdown, self-protection, physical distancing, quarantine, and isolation compartments. Then, we investigate the proposed model through Caputo fractional order derivative. Fixed point theory has been used to analyze the Caputo fractional-order derivative model's existence and uniqueness solutions, whereas the Adams-Bashforth-Moulton numerical scheme was applied for numerical simulation. Driven by extensive theoretical analysis and numerical simulation, this work further illuminates the substantial impact of various control measures.

[Reconstruction of the extensor apparatus with advanced structural defects in knee revision arthroplasty]. / Rekonstruktion des Streckapparates bei fortgeschrittenen Defekten in der Knierevisionsendoprothetik.

OBJECTIVE: Restoration of the extensor apparatus in the case of advanced tendon defects as part of revision total knee arthroplasty (TKA). Reconstruction and augmentation using vascularized gastrocnemius muscle and tendon. INDICATIONS: Advanced degeneration of the extensor apparatus (patella tendon; quadriceps tendon) with or without discontinuity, following revision arthroplasty. CONTRAINDICATIONS: Persistent infection or pending TKA revision. Damaged gastrocnemius or soleus muscle or Achilles tendon. SURGICAL TECHNIQUE: Extension of the surgical TKA-access medial-distally. Separation of the medial gastrocnemius muscle along the raphe and preparation of the distal tendon from the soleus portion. Transposition into the defect site, augmentation or reconstruction of the defect by double turn of the gastrocnemius tendon. The muscle belly serves to adequately cover the tendon as well as the ventral knee joint. Mesh coverage of the muscle. POSTOPERATIVE MANAGEMENT: Immobilization of the knee and ankle for 10 days until mesh graft healing. Stepwise increasing flection of the knee with 30°/60°/90° every 2 weeks. Total weight bearing with secured full extended knee, no weight bearing with flexed knee for 6 weeks. RESULTS: In 9 patients, 3 with complete rupture of the patellar tendon, 5 with destruction of the extensor apparatus, and 1 patient with rupture of the quadriceps tendon following TKA revision, good functional results were achieved with active extension of the knee joint and standing/gait stability 6 months after surgery.