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The study reports the response of first-line antiretroviral therapy (ART) by assessing CD4 and CD8 T-lymphocyte and viral load (VL) among Bangladeshi people living with HIV (PLHIV). This observational approach was conducted on 100 PLHIVs, grouped into therapy naive (n = 33), therapy initiators with CD4 T-cell count of 1 year prior to the study period (n = 34). Therapy initiators who continued the study (n = 20) were followed up after 12 and 24 weeks of therapy initiation. The CD4 and CD8 T-lymphocyte count estimation and (VL) were quantified. The mean CD4 T-lymphocyte count was significantly reduced among the therapy initiators in comparison to therapy naive and therapy receivers. Similar findings were observed for CD8 T-lymphocyte count among the study groups. The mean HIV-1 RNA VL among therapy initiators showed a significant decrease after 12 and 24 weeks, and 85% patients in this group obtained undetectable VL status indicating the good therapeutic outcome.

Based on the clinical evidence that head position measured by the multisensory system contributes to motion control, this study suggests a biomechanical human-central nervous system modeling and control framework for sit-to-stand motion synthesis. Motivated by the evidence for a task-oriented encoding of motion by the central nervous system, we propose a framework to synthesize and control sit-to-stand motion using only head position trajectory in the high-level-task-control environment. First, we design a generalized analytical framework comprising a human biomechanical model and an adaptive neuro-fuzzy inference system to emulate central nervous system. We introduce task-space training algorithm for adaptive neuro-fuzzy inference system training. The adaptive neuro-fuzzy inference system controller is optimized in the number of membership functions and training cycles to avoid over-fitting. Next, we develop custom human models based on anthropometric data of real subjects. Using the weighting coefficient method, we estimate body segment parameter. The subject-specific body segment parameter values are used (1) to scale human model for real subjects and (2) in task-space training to train custom adaptive neuro-fuzzy inference system controllers. To validate our modeling and control scheme, we perform extensive motion capture experiments of sit-to-stand transfer by real subjects. We compare the synthesized and experimental motions using kinematic analyses. Our analytical modeling-control scheme proves to be scalable to real subjects' body segment parameter and the task-space training algorithm provides a means to customize adaptive neuro-fuzzy inference system efficiently. The customized adaptive neuro-fuzzy inference system gives 68%–98% improvement over general adaptive neuro-fuzzy inference system. This study has a broader scope in the fields of rehabilitation, humanoid robotics, and virtual characters' motion planning based on high-level-task-control scheme. [ABSTRACT FROM AUTHOR]