Miracle-dz/UniFAR-FTUs huggingface dataset:
Amino Acid Starvation Sensitizes Resistant Breast Cancer to Doxorubicin-Induced Cell Death

Many clinical trials are beginning to assess the effectiveness of compounds known to regulate autophagy in patients receiving anti-cancer chemotherapy. However, autophagy inhibition, through exogenous inhibitors, or activation, through starvation, has revealed conflicting roles in cancer management and chemotherapeutic outcome. This study aimed to assess the effect of amino acid starvation on doxorubicin-treated breast cancer cells by assessing the roles of autophagy and apoptosis. An in vitro breast cancer model consisting of the normal breast epithelial MCF12A and the metastatic breast cancer MDAMB231 cells was used. Apoptotic and autophagic parameters were assessed following doxorubicin treatments, alone or in combination with bafilomycin, ATG5 siRNA or amino acid starvation. Inhibition of autophagy, through ATG5 siRNA or bafilomycin treatment, increased caspase activity and intracellular doxorubicin concentrations in MCF12A and MDAMB231 cells during doxorubicin treatment. While amino acid starvation increased autophagic activity and decreased caspase activity and intracellular doxorubicin concentrations in MCF12A cells, no changes in autophagic parameters or caspase activity were observed in MDAMB231 cells. Our in vivo data showed that 24 h protein starvation during high dose doxorubicin treatment resulted in increased survival of tumor-bearing GFP-LC3 mice. Results from this study suggest that short term starvation during doxorubicin chemotherapy may be a realistic avenue for adjuvant therapy, especially with regards to the protection of non-cancerous cells. More research is however, needed to fully understand the regulation of autophagic flux during starvation.
Effects of face masks on acoustic analysis and speech perception: Implications for peri-pandemic protocolsa)

Wearing face masks (alongside physical distancing) provides some protection against infection from COVID-19. Face masks can also change how we communicate and subsequently affect speech signal quality. Here we investigated how three face mask types (N95, surgical and cloth) affect acoustic analysis of speech and perceived intelligibility in healthy subjects. We compared speech produced with and without the different masks on acoustic measures of timing, frequency, perturbation and power spectral density. Speech clarity was also examined using a standardized intelligibility tool by blinded raters. Mask type impacted the power distribution in frequencies above 3kHz for both the N95 and surgical masks. Measures of timing and spectral tilt also differed across mask conditions. Cepstral and harmonics to noise ratios remained flat across mask type. No differences were observed across conditions for word or sentence intelligibility measures. Our data show that face masks change the speech signal, but some specific acoustic features remain largely unaffected (e.g., measures of voice quality) irrespective of mask type. Outcomes have bearing on how future speech studies are run when personal protective equipment is worn.
Flow-Induced Transcriptomic Remodeling of Endothelial Cells Derived From Human Induced Pluripotent Stem Cells

The vascular system is essential for the development and function of all organs and tissues in our body. The molecular signature and phenotype of endothelial cells (EC) are greatly affected by blood flow-induced shear stress, which is a vital component of vascular development and homeostasis. Recent advances in differentiation of ECs from human induced pluripotent stem cells (hiPSC) have enabled development of in vitro experimental models of the vasculature containing cells from healthy individuals or from patients harboring genetic variants or diseases of interest. Here we have used hiPSC-derived ECs and bulk- and single-cell RNA sequencing to study the effect of flow on the transcriptomic landscape of hiPSC-ECs and their heterogeneity. We demonstrate that hiPS-ECs are plastic and they adapt to flow by expressing known flow-induced genes. Single-cell RNA sequencing showed that flow induced a more homogenous and homeostatically more stable EC population compared to static cultures, as genes related to cell polarization, barrier formation and glucose and fatty acid transport were induced. The hiPS-ECs increased both arterial and venous markers when exposed to flow. Interestingly, while in general there was a greater increase in the venous markers, one cluster with more arterial-like hiPS-ECs was detected. Single-cell RNA sequencing revealed that not all hiPS-ECs are similar even after sorting, but exposing them to flow increases their homogeneity. Since hiPS-ECs resemble immature ECs and demonstrate high plasticity in response to flow, they provide an excellent model to study vascular development.
Mendelian Randomization Identifies the Potential Causal Impact of Dietary Patterns on Circulating Blood Metabolites

Nutrition plays an important role in the development and progress of several health conditions, but the exact mechanism is often still unclear. Blood metabolites are likely candidates to be mediating these relationships, as their levels are strongly dependent on the frequency of consumption of several foods/drinks. Understanding the causal effect of food on metabolites is thus of extreme importance. To establish these effects we utilized Two-sample Mendelian randomization using the genetic variants associated with dietary traits as instrumental variables. The estimates of single-nucleotide polymorphisms’ effects on exposures were obtained from a recent genome-wide association study (GWAS) of 25 individual and 15 principal-component dietary traits, whereas the ones for outcomes were obtained from a GWAS of 123 blood metabolites measured by nuclear magnetic resonance spectroscopy. We identified 417 potentially causal links between food and metabolites, replicating previous findings, such as the association between increased oily fish consumption and higher DHA, and highlighting several novel associations. Most of the associations were related to very-low-density, intermediate-density (IDL) and low-density lipoproteins (LDL). For example, we found that constituents of IDL particles and large LDL particles were raised by coffee and alcohol while lowered by an overall healthier diet and fruit consumption. Our results represent one of the first examples of the estimates of long-term causal effects of diet on metabolites and start bridging the gap in the mechanistic understanding linking food consumption to its health consequences.
Mechanistic model of nutrient uptake explains dichotomy between marine oligotrophic and copiotrophic bacteria

Marine heterotrophic bacteria use a spectrum of nutrient uptake strategies, from that of copiotrophs—which dominate in nutrient-rich environments—to that of oligotrophs—which dominate in nutrient-poor environments. While copiotrophs possess numerous phosphotransferase systems (PTS), oligotrophs lack PTS and rely on ATP-binding cassette (ABC) transporters, which use binding proteins. Here we present a molecular-level model that explains the dichotomy between oligotrophs and copiotrophs as the consequence of trade-offs between PTS and ABC transport. When we approximate ABC transport in Michaelis–Menten form, we find, contrary to the canonical formulation, that its half-saturation concentration KM is not a constant but instead a function of binding protein abundance. Thus, oligotrophs can attain nanomolar KM values using binding proteins with micromolar dissociation constants and while closely matching transport and metabolic capacities. However, this requires large periplasms and high abundances of binding proteins, whose slow diffusion limits uptake rate. We conclude that the use of binding proteins is critical for oligotrophic survival yet severely constrains maximal growth rates, thus fundamentally shaping the divergent evolution of oligotrophs and copiotrophs.
Quantification of an Elite Futsal Team’s Microcycle External Load by Using the Repetition of High and Very High Demanding Scenarios

The main objective of this study was to describe the repetition of external load high-demanding scenarios and very high-demanding scenarios of match play for velocity, distance, and neuromuscular locomotor variables of an elite futsal team. Additionally, we also checked how these high- and very high-demanding scenarios were distributed throughout the microcycle. The most demanding scenario (measured using a rolling average method with a 1-min time window) of match play was measured out of thirteen elite futsal players using a local positioning system in the course of thirteen official matches and six in-season microcycles. A mean of the top three match play observations for each variable and each player were used to determine the most demanding scenario (100%) reference value. Data were reanalyzed to count the number of high-demanding scenarios (80–90% of the individual most demanding scenario) and very high-demanding scenarios (>90% of the individual most demanding scenario). The number of scenarios was analyzed with respect to the number of days prior to the match [match day (MD) minus X] and a bootstrap confidence interval approach was used to assess differences between MD. During a single match, players have to cope with repeated high- and very high-demanding scenarios. Moreover, the training session 2 days prior to the match was the one most similar to the match, surpassing it only in scenarios of locomotor velocity variables, albeit with significantly fewer scenarios of neuromuscular variables. The number of high- and very high-demanding scenarios in the training session prior to the match dropped significantly in comparison with the rest of the microcycle and the match. This new monitoring method may help practitioners to establish an accurate assessment of external load demands in competition and training.
Gait Progression Over 6 Years in Parkinson’s Disease: Effects of Age, Medication, and Pathology

Background: Gait disturbance is an early, cardinal feature of Parkinson’s disease (PD) associated with falls and reduced physical activity. Progression of gait impairment in Parkinson’s disease is not well characterized and a better understanding is imperative to mitigate impairment. Subtle gait impairments progress in early disease despite optimal dopaminergic medication. Evaluating gait disturbances over longer periods, accounting for typical aging and dopaminergic medication changes, will enable a better understanding of gait changes and inform targeted therapies for early disease. This study aimed to describe gait progression over the first 6 years of PD by delineating changes associated with aging, medication, and pathology. Methods: One-hundred and nine newly diagnosed PD participants and 130 controls completed at least two gait assessments. Gait was assessed at 18-month intervals for up to 6 years using an instrumented walkway to measure sixteen spatiotemporal gait characteristics. Linear mixed-effects models assessed progression. Results: Ten gait characteristics significantly progressed in PD, with changes in four of these characteristics attributable to disease progression. Age-related changes also contributed to gait progression; changes in another two characteristics reflected both aging and disease progression. Gait impairment progressed irrespective of dopaminergic medication change for all characteristics except step width variability. Conclusions: Discrete gait impairments continue to progress in PD over 6 years, reflecting a combination of, and potential interaction between, disease-specific progression and age-related change. Gait changes were mostly unrelated to dopaminergic medication adjustments, highlighting limitations of current dopaminergic therapy and the need to improve interventions targeting gait decline.
The Clinical Features and Prognostic Assessment of SARS-CoV-2 Infection-Induced Sepsis Among COVID-19 Patients in Shenzhen, China

Background: The outbreak of the novel coronavirus disease (COVID-19) that began in December 2019 has posed a great threat to human health and caused a significant loss of life. In Shenzhen, 465 patients were confirmed to have COVID-19 as of August 31, 2020. In the present study, we aimed to describe the clinical characteristics of COVID-19 patients in Shenzhen and identify risk factors for the development of viral sepsis. Methods: In this retrospective study, patients who were confirmed to have a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and were admitted to the Third People's Hospital of Shenzhen from January 11 to April 27, 2020 were included in the cohort. Clinical data were extracted and followed up to May 10, 2020, by using predesigned data collection forms. Results: A total of 422 hospitalized COVID-19 patients were enrolled in this study, including 97 (23%) patients with viral sepsis at hospital admission and 325 (77%) non-septic patients. Patients with sepsis were much older than those without sepsis (57 vs. 43 years, P < 0.001) and presented with more comorbidities. Septic patients showed multiple organ dysfunction and significant abnormalities in immune- and inflammation-related biomarkers, and had poorer outcomes when compared to those without sepsis. Increased levels of interleukin-6, blood urea nitrogen, and creatine kinase were associated with the development of SARS-CoV-2-induced sepsis, and an elevated production of interleukin-6 was found to be an independent risk factor for the progression to critical illness among septic COVID-19 patients. Conclusions: SARS-CoV-2 infection-induced sepsis is critically involved in the severity and prognosis of COVID-19 patients by characterizing both aberrant immune response and uncontrolled inflammation. The development of sepsis might contribute to multiple organ dysfunction and poor outcomes in COVID-19 patients during hospitalization.
A Systemic Review of Available Low-Cost EEG Headsets Used for Drowsiness Detection

Drowsiness is a leading cause of traffic and industrial accidents, costing lives and productivity. Electroencephalography (EEG) signals can reflect awareness and attentiveness, and low-cost consumer EEG headsets are available on the market. The use of these devices as drowsiness detectors could increase the accessibility of safety and productivity-enhancing devices for small businesses and developing countries. We conducted a systemic review of currently available, low-cost, consumer EEG-based drowsiness detection systems. We sought to determine whether consumer EEG headsets could be reliably utilized as rudimentary drowsiness detection systems. We included documented cases describing successful drowsiness detection using consumer EEG-based devices, including the Neurosky MindWave, InteraXon Muse, Emotiv Epoc, Emotiv Insight, and OpenBCI. Of 46 relevant studies, ~27 reported an accuracy score. The lowest of these was the Neurosky Mindwave, with a minimum of 31%. The second lowest accuracy reported was 79.4% with an OpenBCI study. In many cases, algorithmic optimization remains necessary. Different methods for accuracy calculation, system calibration, and different definitions of drowsiness made direct comparisons problematic. However, even basic features, such as the power spectra of EEG bands, were able to consistently detect drowsiness. Each specific device has its own capabilities, tradeoffs, and limitations. Widely used spectral features can achieve successful drowsiness detection, even with low-cost consumer devices; however, reliability issues must still be addressed in an occupational context.
Face neurons in human visual cortex

The exquisite capacity of primates to detect and recognize faces is crucial for social interactions. Although disentangling the neural basis of human face recognition remains a key goal in neuroscience, direct evidence at the single-neuron level is virtually nonexistent. We recorded from face-selective neurons in human visual cortex, in a region characterized by functional magnetic resonance imaging (fMRI) activations for faces compared to objects (i.e. the occipital face area, OFA). The majority of visually responsive neurons in this fMRI activation showed strong selectivity at short latencies for faces compared to objects. Feature scrambled faces and face-like objects could also drive these neurons, suggesting that the OFA is not tightly-tuned to the visual attributes that typically define whole human faces. These single-cell recordings within the human face processing system provide vital experimental evidence linking previous imaging studies in humans and invasive studies in animal models.
Neuronal Parasitism, Early Myenteric Neurons Depopulation and Continuous Axonal Networking Damage as Underlying Mechanisms of the Experimental Intestinal Chagas' Disease

There is a growing consensus that the balance between the persistence of infection and the host immune response is crucial for chronification of Chagas heart disease. Extrapolation for chagasic megacolon is hampered because research in humans and animal models that reproduce intestinal pathology is lacking. The parasite-host relationship and its consequence to the disease are not well-known. Our model describes the temporal changes in the mice intestine wall throughout the infection, parasitism, and the development of megacolon. It also presents the consequence of the infection of primary myenteric neurons in culture with Trypanosoma cruzi (T. cruzi). Oxidative neuronal damage, involving reactive nitrogen species induced by parasite infection and cytokine production, results in the denervation of the myenteric ganglia in the acute phase. The long-term inflammation induced by the parasite's DNA causes intramuscular axonal damage, smooth muscle hypertrophy, and inconsistent innervation, affecting contractility. Acute phase neuronal loss may be irreversible. However, the dynamics of the damages revealed herein indicate that neuroprotection interventions in acute and chronic phases may help to eradicate the parasite and control the inflammatory-induced increase of the intestinal wall thickness and axonal loss. Our model is a powerful approach to integrate the acute and chronic events triggered by T. cruzi, leading to megacolon.
Control of Blood Glucose Level for Type 1 Diabetes Mellitus using Improved Hovorka Equations: Comparison between Clinical and In-Silico Works

Background Type 1 diabetes mellitus (T1DM) occurs due to inability of the body to produce sufficient amount of insulin to regulate blood glucose level (BGL) at normoglycemic range between 4.0 to 7.0 mmol/L. Thus, T1DM patients require to do self-monitoring blood glucose (SMBG) via finger pricks and depend on exogenous insulin injection to maintain their BGL which is very painful and exasperating. Ongoing works on artificial pancreas device nowadays focus primarily on a computer algorithm which is programmed into the controller device. This study aims to simulate so-called improved equations from the Hovorka model using actual patients’ data through in-silico works and compare its findings with the clinical works. Methods The study mainly focuses on computer simulation in MATLAB using improved Hovorka equations in order to control the BGL in T1DM. The improved equations can be found in three subsystems namely; glucose, insulin and insulin action subsystems. CHO intakes were varied during breakfast, lunch and dinner times for three consecutive days. Simulated data are compared with the actual patients’ data from the clinical works. Results Result revealed that when the patient took 36.0g CHO during breakfast and lunch, the insulin administered was 0.1U/min in order to maintain the blood glucose level (BGL) in the safe range after meal; while during dinner time, 0.083U/min to 0.1 U/min of insulins were administered in order to regulate 45.0g CHO taken during meal. The basal insulin was also injected at 0.066U/min upon waking up time in the early morning. The BGL was able to remain at normal range after each meal during in-silico works compared to clinical works. Conclusions This study proved that the improved Hovorka equations via in-silico works can be employed to model the effect of meal disruptions on T1DM patients, as it demonstrated better control as compared to the clinical works.
Distinct Human Gut Microbial Taxonomic Signatures Uncovered With Different Sample Processing and Microbial Cell Disruption Methods for Metaproteomic Analysis

Metaproteomics is as a promising technique for studying the human gut microbiota, because it can reveal the taxonomic profile and also shed light on the functional role of the microbial community. Nevertheless, methods for extracting proteins from stool samples continue to evolve, in the pursuit of optimal protocols for moistening and dispersing the stool sample and for disrupting microbial cells which are two critical steps for ensuring good protein recovery. Here, we evaluated different stool sample processing and microbial cell disruption methods for metaproteomic analyses of human gut microbiota. An unsupervised principal component analysis showed that different methods produced similar human gut microbial taxonomic profiles. An unsupervised two-way hierarchical clustering analysis identified the microbial taxonomic signatures associated with each method. Proteobacteria and Bacteroidetes identification was favored by moistening the stool samples during processing and by disrupting cells with medium-sized glass beads. Ascomycota identification was enhanced by using large-sized glass beads during sample processing for stool dispersion. Euryarchaeota identification was improved with a combination of small and medium-sized glass beads for cell disruption. Assessments of the relative abundance of Firmicutes, Actinobacteria and Spirochaetes improved when ultrasonication was performed before cell disruption with glass beads. The latter method also increased the overall number of identified proteins. Taxonomic and protein functional analyses of metaproteomic data derived from stool samples from six healthy individuals showed common taxonomic profiles. We also detected certain proteins involved in microbial functions relevant to the host and related mostly to particular taxa, such as B12 biosynthesis and short chain fatty acid production carried out mainly by members in the Prevotella genus and the Firmicutes phylum, respectively. Finally, in this metaproteomic study we identified several human proteins, mostly related to the anti-microbial response, which could contribute to determining the beneficial and detrimental relationships between gut microbiota and human cells in particular human diseases.