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Depression is one of the most overlooked psychiatric disorders among middle-aged and elderly men. Saffron (Crocus sativus L.) stigmas are a favorite and common spice that is used as an antidepressant, an anti-inflammatory and a sexual stimulant. This systematic review aimed to provide a comprehensive survey of the effects of saffron on depression in middle-aged and elderly men.

The authors searched Web of Science, PubMed, ProQuest, Cochrane, Science Direct, Scopus and Google Scholar databases to gather any relevant studies published from 1980 to September 2022. This review included studies that examined the relationship between depression and testosterone concentration (four studies) or saffron’s effects on testosterone and depression (six studies).

Observational research showed an association between testosterone and depression in middle-aged and elderly men. Furthermore, animal studies have found that saffron increased testosterone levels and decreased depression in males. There is evidence that testosterone may contribute to the mood. Besides its antioxidant and anti-inflammatory properties, this study proposed scientific mechanisms that saffron may also play a protective role against depression through its influence on testosterone, in middle-aged and elderly men. The general use of saffron at high doses or for prolonged periods may cause side effects; therefore, it is important to consult a health-care professional before the recommended dosage and duration of saffron supplementation, especially if a person has any underlying health conditions or is taking medications.

This study provides researchers with an additional perspective for conducting clinical trials on the use of saffron to enhance the mood of elderly men with testosterone deficiency.

This study proposes strategies for vaccine center allocation for coronavirus disease (COVID) vaccine by determining the number of vaccination stations required for the vaccination drive, location of vaccination station, assignment of demand group to vaccination station, allocation of the scarce medical professional teams to station and number of optimal days a vaccination station to be functional in a week.

The authors propose a mixed-integer nonlinear programming model. However, to handle nonlinearity, the authors devise a heuristic and then propose a two-stage mixed-integer linear programming (MILP) formulation to optimize the allocation of vaccination centers or stations to demand groups in the first stage and the allocation of vaccination centers to cold storage links in the second stage. The first stage optimizes the cost and average distance traveled by people to reach the vaccination center, whereas the second stage optimizes the vaccine’s holding and storage and transportation cost by efficiently allocating cold storage links to the centers.

The model is studied for the real-world case of Chandigarh, India. The results obtained validate that the proposed approach can immensely help government agencies and policymaking body for a successful vaccination drive. The model tries to find a tradeoff between loss due to underutilized medical teams and the distance traveled by a demand group to get the vaccination.

To the best of our knowledge, there are hardly any studies on a vaccination program at such a scale due to sudden outbreaks such as Covid-19.

Prison settings have limited resources, and it is of particular interest to analyze which antipsychotics are commonly prescribed in these conditions and to determine the prevalence of the adverse effects.

A cross-sectional, epidemiological survey was used to measure the prevalence of antipsychotic prescribing among adult prisoners in Sremska Mitrovica Prison in 2020.

The prevalence of antipsychotic use was 7.58%. The most commonly prescribed antipsychotic was clozapine (45.36%), but also olanzapine, haloperidol and risperidone were prescribed. The incidence of extrapyramidal adverse effects was nonexistent and the metabolic parameters did not differ between participants using metabolic syndrome–inducing antipsychotics and those who were prescribed metabolically inert medications. The prescribed doses were lower compared with the recommended.

This research includes certain points that should be cautiously considered. First, the data were cross-sectional and the findings did not provide causal interpretations. Second, the data are from a single penitentiary institution, albeit the largest in the country; however, that may affect the generalizability of the findings. Third, because the included subjects were not hospitalized, some laboratory analyses were not available, according to the local regulations, and thus the prevalence of metabolic syndrome could not be precisely determined.

The prevalence of the antipsychotic use in prison environment is significantly higher than in general population. The most frequently prescribed antipsychotics are clozapine and olanzapine. The prevalence of adverse effects is rare, however, that is possibly due to low doses of the prescribed antipsychotics. The list of therapeutic options available to the incarcerated persons in this facility is also limited. The list of available antipsychotics does not include some atypical antipsychotics with more favorable safety and tolerability profile, such as aripiprazole or cariprazine. Long-acting antipsychotic injectables were also not available to these patients. Laboratory analyses are not regularly conducted and do not include some essential parameters such as lipid status or differential blood count. Low-dose antipsychotics for behavioral symptoms appears to be well tolerated under prison conditions where adherence is assured. It is effective during the prison stay but long-term effects, especially after release from prison, had not been studied.

This paper advocates for better quality of health care in this correctional facility: more therapeutic options and better laboratory monitoring. The authors justify the use of clozapine in this settings due its benefits in reducing violence and aggression; however, further research would be necessary to clarify does the use of clozapine in incarcerated persons cause behavioral improvements that could result in shorter incarcerations, less recidivism and better quality of life.

To the best of the authors’ knowledge, this is the first insight of the antipsychotic prescribing practice in Serbia. There is very limited data on prisoners’ health care, especially mental health care, in Balkan countries. The antipsychotic prescribing pattern in this sample is characterized with higher than expected clozapine use, but without expected adverse effects.

The devastating acute COVID-19 epidemic crippled the global economy in 2020. Within a month of the COVID-19 epidemic, every industry saw its stock prices plummet the most. Ending the COVID-19 pandemic will need equitable access to safe and effective vaccinations. This study aims to look at the link between COVID-19 vaccination and the stock markets for health and pharmaceutical sector.

The researchers used a mean-adjusted return model and event research approach to figure out how the first dose of the COVID-19 vaccine affects health and pharmaceutical sector stock market returns.

The evidence-based outcome indicates that immunisation announcement affects health and pharmaceutical company returns. Furthermore, the study concludes that the health and pharmaceutical industry is inefficient for a short period of time, but after 41 days, the sector absorbs the noisy information.

Since the outbreak, the development of COVID-19 vaccines has been a key focus of shareholders and investors. This study is unique in that it investigates the effect of the first dosage of SARS-CoV-2 vaccination on equity returns in the health and pharmaceutical industries, and it is likely to make a substantial contribution to the capital market literature on event methodology.

Background: Compliance of patients with self-care practices is the mainstay of measures to manage diabetes. This study explored self-care practices of type 2 diabetes patients receiving insulin treatment in North-East Ethiopia.

Methods: The study employed an interpretive phenomenological approach using purposive sampling. The data were collected from 24 (11 males and 13 females) participants (July 2019 to January 2020) using in-depth interviews till theoretical saturation. The data were analyzed thematically and organized using QDA Miner Lite v2.0.8.

Results: The findings were categorized into: labeling diabetes, self-care maintenance experiences, recognition of symptoms, and mitigating mechanisms. The self-care maintenance practice of patients was linked with diet input preferences and the effectiveness level of insulin. What guides the self-care behavior was the patients’ preferentiality of strictly adhering to their preferred dietary inputs. Barley and wheat were the most common preferential and non-preferential inputs, respectively. The patients strictly adhered to insulin treatment because they found it effective. The most common hyperglycemia symptoms to be managed by taking an additional dose of insulin, were frequent urination, increased thirst, and their consequence (dehydration). Excessive sweating (initial), shivering (middle), and falling (final), respectively in severity, were the most common symptoms of hypoglycemia which were perceived to be treated with sweet snacks.

Originality: To our knowledge, this is the first research in Ethiopia to investigate the self-care experiences of type 2 diabetes patients receiving insulin using an interpretive phenomenological approach.

The purpose of this paper is to systematically review the functionalities, safety regulations and product applications of herb Stevia rebaudiana extract. This plant material is embedded with multiple functionalities such as antioxidant, antidiabetics, anti-inflammation and antimicrobial. The regulations released from global authorities are covered to ensure the safety premise of stevia. Besides, the product applications of the extract of aerial parts of the herb S. rebaudiana helps us to recognize its value from commercial side.

Relevant literatures are selected and obtained from main scientific databases such as Google Scholar, Web of Science, PubMed and trade magazines published between 2000 and 2023. The keywords and their possible combinations such as sweetening, antioxidant, antidiabetics, anti-inflammation, safety and product development were used to ensure the preciseness and completeness of literature searching. Major data such as sweetness, total phenolic content and dose together with latter critical conclusions from searched publications were appropriately used and discussed. In this review, approximately 150 scientific literatures were meticulously ordered and analyzed. In applications, it is the first time that sentiment analysis was used to obtain a market assessment of the stevia-containing products.

This review paper helps rearrange the scientific affairs of those stevia extract’s functions like sweetening, antioxidant, antidiabetics and inflammation. Sweetness indexes of steviol glycosides were summarized together for comparison while various in vitro and in vivo approaches were reviewed to quantify those functions’ capacities and to depict the related mechanism. The regulation of steviol glycoside compounds such as rebaudioside A was established by global authorities such as US Food and Drug Administration and Joint FAO/World Health Organization Expert Committee to ensure the safety endorsement before commercialization. Then, this study discussed about the market performance of stevia ingredients or products with the self-developed data analytics. This study also investigated the product development progress of stevia-containing food products in the categories of beverage, bakery, dairy and confectionery. Those stevia-containing food consumer goods can be acceptable by certain consumers.

This review paper precisely presents the evidential information about the stevia’s multiple functionalities with mechanisms and global regulation milestones. To the best of the authors’ knowledge, it is then the first time to probe the stevia-containing products’ market performance through data analytics.

This study’s aim is to introduce a high-performance sorbent for the removal of both anionic (Congo red; CR) and cationic (methylene blue; MB) dyes from aqueous solutions.

Sodium silicate is adopted as a substrate for GO and AgNPs with positive charge are used as modifiers. The synthesized nanocomposite is characterized by FTIR, FESEM, EDS, BET and XRD techniques. Then, some of the most effective parameters on the removal of CR and MB dyes such as solution pH, sorbent dose, adsorption equilibrium time, primary dye concentration and salt effect are optimized using the spectrophotometry technique.

The authors successfully achieved notable maximum adsorption capacities (Qmax) of CR and MB, which were 41.15 and 37.04 mg g⁻¹, respectively. The required equilibrium times for maximum efficiency of the developed sorbent were 10 and 15 min for CR and MB dyes, respectively. Adsorption equilibrium data present a good correlation with Langmuir isotherm, with a correlation coefficient of R2 = 0.9924 for CR and R2 = 0.9904 for MB, and kinetic studies prove that the dye adsorption process follows pseudo second-order models (CR R2 = 0.9986 and MB R2 = 0.9967).

The results showed that the proposed mechanism for the function of the developed sorbent in dye adsorption was based on physical and multilayer adsorption for both dyes onto the active sites of non-homogeneous sorbent.

The as-prepared nano-adsorbent has a high ability to remove both cationic and anionic dyes; moreover, to the high efficiency of the adsorbent, it has been tried to make its synthesis steps as simple as possible using inexpensive and available materials.

The complexity of atmospheric corrosion, further compounded by the effects of climate change, makes existing models inappropriate for corrosion prediction. The commonly used kinetic model and dose-response functions are restricted in their capacity to represent the non-linear behaviour of corrosion phenomena. The application of artificial intelligence (AI)-driven machine learning algorithms to corrosion data can better represent the corrosion mechanism by considering the dynamic behaviour due to changing climatic conditions. Effective use of materials, coating systems and maintenance strategies can then be made with such a corrosivity model. Accurate corrosion prediction will help to improve climate change resilience of the social, economic and energy infrastructure in line with the UN Sustainable Development Goals (SDGs) 7 (Affordable and Clean Energy), 9 (Industry, Innovation and Infrastructure) and 13 (Climate Action). This chapter discusses atmospheric corrosion prediction in relation to the SDGs and the influence of AI in overcoming the challenges.