Dynamic Computational Model Suggests That Cellular Citizenship Is Fundamental for Selective Tumor Apoptosis

Computational models in the field of cancer research have focused primarily on estimates of biological events based on laboratory generated data. We introduce a novel in-silico technology that takes us to the next level of prediction models and facilitates innovative solutions through the mathematical system. The model's building blocks are cells defined phenotypically as normal or tumor, with biological processes translated into equations describing the life protocols of the cells in a quantitative and stochastic manner. The essentials of communication in a society composed of normal and tumor cells are explored to reveal “protocols” for selective tumor eradication. Results consistently identify “citizenship properties” among cells that are essential for the induction of healing processes in a healthy system invaded by cancer. These properties act via inter-cellular communication protocols that can be optimized to induce tumor eradication along with system recovery. Within the computational systems, the protocols universally succeed in removing a wide variety of tumors defined by proliferation rates, initial volumes, and apoptosis resistant phenotypes; they show high adaptability for biological details and allow incorporation of population heterogeneity. These protocols work as long as at least 32% of cells obey extra-cellular commands and at least 28% of cancer cells report their deaths. This low percentage implies that the protocols are resilient to the suboptimal situations often seen in biological systems. We conclude that our in-silico model is a powerful tool to investigate, to propose, and to exercise logical anti-cancer solutions. Functional results should be confirmed in a biological system and molecular findings should be loaded into the computational model for the next level of directed experiments

Processes and dynamics of linkage to care from mobile/outreach and facility‑based HIV testing models in hard‑to‑reach settings in rural Tanzania. Qualitative findings of a mixed methods study

BACKGROUND: Like other countries, Tanzania instituted mobile and outreach testing approaches to address low HIV testing rates at health facilities and enhance linkage to care. Available evidence from hard-to-reach rural settings of Mbeya region, Tanzania suggests that clients testing HIV+ at facility-based sites are more likely to link to care, and to link sooner, than those testing at mobile sites. This paper (1) describes the populations accessing HIV testing at mobile/outreach and facility-based testing sites, and (2) compares processes and dynamics from testing to linkage to care between these two testing models from the same study context. METHODS: An explanatory sequential mixed-method study (a) reviewed records of all clients (n = 11,773) testing at 8 mobile and 8 facility-based testing sites over 6 months; (b), reviewed guidelines; (c) observed HIV testing sites (n = 10) and Care and Treatment Centers (CTCs) (n = 8); (d) applied questionnaires at 0, 3 and 6 months to a cohort of 1012 HIV newly-diagnosed clients from the 16 sites; and (e) conducted focus group discussions (n = 8) and in-depth qualitative interviews with cohort members (n = 10) and health care providers (n = 20). RESULTS: More clients tested at mobile/outreach than facility-based sites (56% vs 44% of 11,733, p < 0.001). Mobile site clients were more likely to be younger and male (p < 0.001). More clients testing at facility sites were HIV positive (21.5% vs. 7.9% of 11,733, p < 0.001). All sites in both testing models adhered to national HIV testing and care guidelines. Staff at mobile sites showed more proactive efforts to support linkage to care, and clients report favouring the confidentiality of mobile sites to avoid stigma. Clients who tested at mobile/outreach sites faced longer delays and waiting times at treatment sites (CTCs). CONCLUSIONS: Rural mobile/outreach HIV testing sites reach more people than facility based sites but they reach a different clientèle which is less likely to be HIV +ve and appears to be less “linkage-ready”. Despite more proactive care and confidentiality at mobile sites, linkage to care is worse than for clients who tested at facility-based sites. Our findings highlight a combination of (a) patient-level factors, including stigma; and (b) well-established procedures and routines for each step between testing and initiation of treatment in facility-based sites. Long waiting times at treatment sites are a further barrier that must be addressed

Prevalence and determinants of unintended pregnancies amongst women attending antenatal clinics in Pakistan

Background: Unintended pregnancies are a global public health concern and contribute significantly to adverse maternal and neonatal health, social and economic outcomes and increase the risks of maternal deaths and neonatal mortality. In countries like Pakistan where data for the unintended pregnancies is scarce, studies are required to estimate its accurate prevalence and predictors using more specific tools such as the London Measure of Unplanned Pregnancies (LMUP). Methods: We conducted a hospital based cross sectional survey in two tertiary care hospitals in Pakistan. We used a pre tested structured questionnaire to collect the data on socio-demographic characteristics, reproductive history, awareness and past experience with contraceptives and unintended pregnancies using six item the LMUP. We used Univariate and multivariate analysis to explore the association between unintended pregnancies and predictor variables and presented the association as adjusted odds ratios. We also evaluated the psychometric properties of the Urdu version of the LMUP. Results: Amongst 3010 pregnant women, 1150 (38.2%) pregnancies were reported as unintended. In the multivariate analysis age \u3c 20 years (AOR 3.5 1.1-6.5), being illiterate (AOR 1.9 1.1-3.4), living in a rural setting (1.7 1.2-2.3), having a pregnancy interval of = \u3c 12 months (AOR 1.7 1.4-2.2), having a parity of \u3e2 (AOR 1.4 1.2-1.8), having no knowledge about contraceptive methods (AOR 3.0 1.7-5.4) and never use of contraceptive methods (AOR 2.3 1.4-5.1) remained significantly associated with unintended pregnancy. The Urdu version of the LMUP scale was found to be acceptable, valid and reliable with the Cronbach\u27s alpha of 0.85. Conclusions: This study explores a high prevalence of unintended pregnancies and important factors especially those related to family planning. Integrated national family program that provides contraceptive services especially the modern methods to women during pre-conception and post-partum would be beneficial in averting unintended pregnancies and their related adverse outcomes in Pakistan

Steering Evolution with Sequential Therapy to Prevent the Emergence of Bacterial Antibiotic Resistance

The increasing rate of antibiotic resistance and slowing discovery of novel antibiotic treatments presents a growing threat to public health. Here, we consider a simple model of evolution in asexually reproducing populations which considers adaptation as a biased random walk on a fitness landscape. This model associates the global properties of the fitness landscape with the algebraic properties of a Markov chain transition matrix and allows us to derive general results on the non-commutativity and irreversibility of natural selection as well as antibiotic cycling strategies. Using this formalism, we analyze 15 empirical fitness landscapes of E. coli under selection by different β-lactam antibiotics and demonstrate that the emergence of resistance to a given antibiotic can be either hindered or promoted by different sequences of drug application. Specifically, we demonstrate that the majority, approximately 70%, of sequential drug treatments with 2–4 drugs promote resistance to the final antibiotic. Further, we derive optimal drug application sequences with which we can probabilistically ‘steer’ the population through genotype space to avoid the emergence of resistance. This suggests a new strategy in the war against antibiotic–resistant organisms: drug sequencing to shepherd evolution through genotype space to states from which resistance cannot emerge and by which to maximize the chance of successful therapy