A Bio-Social Review to Mitigate the Punishment of Unwanted Acts

Commitment of crime and exhibition of antisocial behavior have been considered as negative acts from early times of human civilization. Recent scientific advances have identified contributions of biological and sociological (environmental factors) factors in forming a maladaptive behavior. Generally, it is accepted by many scholars that punishing a wrongdoer, who has committed a crime owing to genetic predispositions and environmental elements, is not effective and forms of treatments should be replaced to avoid repeating a crime. Moreover, by identifying genetic deficiencies in an individual, an antisocial behavior could be potentially predicted and prevented before it comes to pass. On a whole, genetic and environmental factors, sometimes solely and some other times collaboratively, lead a person to act against society norms. In summary, this body of literature offers examples that explain factors which contribute to committing crimes and approaches which inhibit antisocial behavior. With regard to these aims, we suggest that punishment of criminals who are predisposed genetically in the same manner as other delinquencies is not justifiable and a reduction of punishment should be applied to such individuals. Moreover, by eliminating each of negative elements which contribute to antisocial behavior or crime, we can be more certain that the offender will not repeat antisocial acts after being released

Burden of disease scenarios for 204 countries and territories, 2022–2050: a forecasting analysis for the Global Burden of Disease Study 2021

Background: Future trends in disease burden and drivers of health are of great interest to policy makers and the public at large. This information can be used for policy and long-term health investment, planning, and prioritisation. We have expanded and improved upon previous forecasts produced as part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) and provide a reference forecast (the most likely future), and alternative scenarios assessing disease burden trajectories if selected sets of risk factors were eliminated from current levels by 2050. Methods: Using forecasts of major drivers of health such as the Socio-demographic Index (SDI; a composite measure of lag-distributed income per capita, mean years of education, and total fertility under 25 years of age) and the full set of risk factor exposures captured by GBD, we provide cause-specific forecasts of mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) by age and sex from 2022 to 2050 for 204 countries and territories, 21 GBD regions, seven super-regions, and the world. All analyses were done at the cause-specific level so that only risk factors deemed causal by the GBD comparative risk assessment influenced future trajectories of mortality for each disease. Cause-specific mortality was modelled using mixed-effects models with SDI and time as the main covariates, and the combined impact of causal risk factors as an offset in the model. At the all-cause mortality level, we captured unexplained variation by modelling residuals with an autoregressive integrated moving average model with drift attenuation. These all-cause forecasts constrained the cause-specific forecasts at successively deeper levels of the GBD cause hierarchy using cascading mortality models, thus ensuring a robust estimate of cause-specific mortality. For non-fatal measures (eg, low back pain), incidence and prevalence were forecasted from mixed-effects models with SDI as the main covariate, and YLDs were computed from the resulting prevalence forecasts and average disability weights from GBD. Alternative future scenarios were constructed by replacing appropriate reference trajectories for risk factors with hypothetical trajectories of gradual elimination of risk factor exposure from current levels to 2050. The scenarios were constructed from various sets of risk factors: environmental risks (Safer Environment scenario), risks associated with communicable, maternal, neonatal, and nutritional diseases (CMNNs; Improved Childhood Nutrition and Vaccination scenario), risks associated with major non-communicable diseases (NCDs; Improved Behavioural and Metabolic Risks scenario), and the combined effects of these three scenarios. Using the Shared Socioeconomic Pathways climate scenarios SSP2-4.5 as reference and SSP1-1.9 as an optimistic alternative in the Safer Environment scenario, we accounted for climate change impact on health by using the most recent Intergovernmental Panel on Climate Change temperature forecasts and published trajectories of ambient air pollution for the same two scenarios. Life expectancy and healthy life expectancy were computed using standard methods. The forecasting framework includes computing the age-sex-specific future population for each location and separately for each scenario. 95% uncertainty intervals (UIs) for each individual future estimate were derived from the 2·5th and 97·5th percentiles of distributions generated from propagating 500 draws through the multistage computational pipeline. Findings: In the reference scenario forecast, global and super-regional life expectancy increased from 2022 to 2050, but improvement was at a slower pace than in the three decades preceding the COVID-19 pandemic (beginning in 2020). Gains in future life expectancy were forecasted to be greatest in super-regions with comparatively low life expectancies (such as sub-Saharan Africa) compared with super-regions with higher life expectancies (such as the high-income super-region), leading to a trend towards convergence in life expectancy across locations between now and 2050. At the super-region level, forecasted healthy life expectancy patterns were similar to those of life expectancies. Forecasts for the reference scenario found that health will improve in the coming decades, with all-cause age-standardised DALY rates decreasing in every GBD super-region. The total DALY burden measured in counts, however, will increase in every super-region, largely a function of population ageing and growth. We also forecasted that both DALY counts and age-standardised DALY rates will continue to shift from CMNNs to NCDs, with the most pronounced shifts occurring in sub-Saharan Africa (60·1% [95% UI 56·8–63·1] of DALYs were from CMNNs in 2022 compared with 35·8% [31·0–45·0] in 2050) and south Asia (31·7% [29·2–34·1] to 15·5% [13·7–17·5]). This shift is reflected in the leading global causes of DALYs, with the top four causes in 2050 being ischaemic heart disease, stroke, diabetes, and chronic obstructive pulmonary disease, compared with 2022, with ischaemic heart disease, neonatal disorders, stroke, and lower respiratory infections at the top. The global proportion of DALYs due to YLDs likewise increased from 33·8% (27·4–40·3) to 41·1% (33·9–48·1) from 2022 to 2050, demonstrating an important shift in overall disease burden towards morbidity and away from premature death. The largest shift of this kind was forecasted for sub-Saharan Africa, from 20·1% (15·6–25·3) of DALYs due to YLDs in 2022 to 35·6% (26·5–43·0) in 2050. In the assessment of alternative future scenarios, the combined effects of the scenarios (Safer Environment, Improved Childhood Nutrition and Vaccination, and Improved Behavioural and Metabolic Risks scenarios) demonstrated an important decrease in the global burden of DALYs in 2050 of 15·4% (13·5–17·5) compared with the reference scenario, with decreases across super-regions ranging from 10·4% (9·7–11·3) in the high-income super-region to 23·9% (20·7–27·3) in north Africa and the Middle East. The Safer Environment scenario had its largest decrease in sub-Saharan Africa (5·2% [3·5–6·8]), the Improved Behavioural and Metabolic Risks scenario in north Africa and the Middle East (23·2% [20·2–26·5]), and the Improved Nutrition and Vaccination scenario in sub-Saharan Africa (2·0% [–0·6 to 3·6]). Interpretation: Globally, life expectancy and age-standardised disease burden were forecasted to improve between 2022 and 2050, with the majority of the burden continuing to shift from CMNNs to NCDs. That said, continued progress on reducing the CMNN disease burden will be dependent on maintaining investment in and policy emphasis on CMNN disease prevention and treatment. Mostly due to growth and ageing of populations, the number of deaths and DALYs due to all causes combined will generally increase. By constructing alternative future scenarios wherein certain risk exposures are eliminated by 2050, we have shown that opportunities exist to substantially improve health outcomes in the future through concerted efforts to prevent exposure to well established risk factors and to expand access to key health interventions

Genetic Technologies and Ethics

In the past decade, the human genome has been completely sequenced and the knowledge from it has begun to influence the fields of biological and social sciences in fundamental ways. Identification of about 25000 genes in the human genome is expected to create great benefits in diagnosis and treatment of diseases in the coming years. However, Genetic technologies have also created many interesting and difficult ethical issues which can affect the human societies now and in the future. Application of genetic technologies in the areas of stem cells, cloning, gene therapy, genetic manipulation, gene selection, sex selection and preimplantation diagnosis has created a great potential for the human race to influence and change human life on earth as we know it today. Therefore, it is important for leaders of societies in the modern world to pay attention to the advances in genetic technologies and prepare themselves and those institutions under their command to face the challenges which these new technologies induce in the areas of ethics, law and social policies

The Role of Genomics and Proteomics in Lung Cancer Early Detection and Treatment

Lung cancer is the leading cause of cancer-related death worldwide, with non-small-cell lung cancer (NSCLC) being the primary type. Unfortunately, it is often diagnosed at advanced stages, when therapy leaves patients with a dismal prognosis. Despite the advances in genomics and proteomics in the past decade, leading to progress in developing tools for early diagnosis, targeted therapies have shown promising results; however, the 5-year survival of NSCLC patients is only about 15%. Low-dose computed tomography or chest X-ray are the main types of screening tools. Lung cancer patients without specific, actionable mutations are currently treated with conventional therapies, such as platinum-based chemotherapy; however, resistances and relapses often occur in these patients. More noninvasive, inexpensive, and safer diagnostic methods based on novel biomarkers for NSCLC are of paramount importance. In the current review, we summarize genomic and proteomic biomarkers utilized for the early detection and treatment of NSCLC. We further discuss future opportunities to improve biomarkers for early detection and the effective treatment of NSCLC

Surfing the clinical trials of ECG teaching to medical students: A systematic review

Interpreting an electrocardiogram (ECG) is crucial for every physician. The physician's competency in ECG interpretation needs to be improved at any stage of medical education. The aim of the present study was to review the currently published clinical trials of ECG teaching to medical students and provide suggestions for future works. On May 1, 2022, PubMed, Scopus, Web of Science, Google Scholar, and ERIC were searched to retrieve relevant articles on clinical trials of ECG teaching to medical students. The quality of the included studies was assessed utilizing the Buckley et al. criteria. The screening, data extraction, and quality appraisal processes were duplicated independently. In case of disagreements, consultation with a third author was put forth. In total, 861 citations were found in the databases. After screening abstracts and full texts, 23 studies were deemed eligible. The majority of the studies were of good quality. Peer teaching (7 studies), self-directed learning (6 studies), web-based learning (10 studies), and various assessment modalities (3 studies) comprised the key themes of the studies. Various methods of ECG teaching were encountered in the reviewed studies. Future studies in ECG training should focus on novel and creative teaching methods, the extent to which self-directed learning can be effective, the utility of peer teaching, and the implications of computer-assisted ECG interpretation (e.g., artificial intelligence) for medical students. Long-term knowledge retention assessment studies based on different approaches integrated with clinical outcomes could be beneficial in determining the most efficient modalities

Effect of Chronic Restraint Stress on HPA Axis Activity and Expression of BDNF and Trkb in the Hippocampus of Pregnant Rats: Possible Contribution in Depression during Pregnancy and Postpartum Period

Introduction: Brain-Derived Neurotrophic Factor (BDNF) and its receptor, TrkB, in the hippocampus are targets for adverse effects of stress paradigms in addition, BDNF and its receptor play key role in the pathology of brain diseases like depression. In the present study, we evaluated the possible role of hippocampal BDNF in depression during pregnancy, Methods: To achieve the purpose, repeated restrain stress (1 or 3 hours daily for 7 days) during the last week of pregnancy was used and alteration in the gene expression of hippocampal BDNF and TrkB evaluated by semi-quantitative PCR. Results: The results showed that in stress group the level of ACTH and Corticosterone is increased showing that our model was efficient in inducing psychological stress we also found that BDNF and TrkB expression are decreased in 3 hours stress group but not in 1 hour stress compared to control group. Discussion: Our results imply that decrease in BDNF and its receptor could contribute in some adverse effects of stress during pregnancy such as elevation of depressive like behavior

Molecular signatures of anthroponotic cutaneous leishmaniasis in the lesions of patients infected with Leishmania tropica

Anthroponotic cutaneous leishmaniasis (CL) caused by Leishmania tropica (L. tropica) represents a public health challenge in several resource poor settings. We herein employed a systems analysis approach to study molecular signatures of CL caused by L. tropica in the skin lesions of ulcerative CL (UCL) and non-ulcerative CL (NUCL) patients. Results from RNA-seq analysis determined shared and unique functional transcriptional pathways in the lesions of the UCL and NUCL patients. Several transcriptional pathways involved in inflammatory response were positively enriched in the CL lesions. A multiplexed inflammatory protein analysis showed differential profiles of inflammatory cytokines and chemokines in the UCL and NUCL lesions. Transcriptional pathways for Fcγ receptor dependent phagocytosis were among shared enriched pathways. Using L. tropica specific antibody (Ab)-mediated phagocytosis assays, we could substantiate Ab-dependent cellular phagocytosis (ADCP) and Ab-dependent neutrophil phagocytosis (ADNP) activities in the lesions of the UCL and NUCL patients, which correlated with L. tropica specific IgG Abs. Interestingly, a negative correlation was observed between parasite load and L. tropica specific IgG/ADCP/ADNP in the skin lesions of CL patients. These results enhance our understanding of human skin response to CL caused by L. tropica.ISSN:2045-232

MECHANISMS OF DISEASE Mechanisms of disease Use of proteomic patterns in serum to identify ovarian cancer

Background New technologies for the detection of earlystage ovarian cancer are urgently needed. Pathological changes within an organ might be reflected in proteomic patterns in serum. We developed a bioinformatics tool and used it to identify proteomic patterns in serum that distinguish neoplastic from non-neoplastic disease within the ovary. Methods Proteomic spectra were generated by mass spectroscopy (surface-enhanced laser desorption and ionisation). A preliminary “training ” set of spectra derived from analysis of serum from 50 unaffected women and 50 patients with ovarian cancer were analysed by an iterative searching algorithm that identified a proteomic pattern that completely discriminated cancer from noncancer. The discovered pattern was then used to classif