Stochastic IMT (insulator-metal-transition) neurons: An interplay of thermal and threshold noise at bifurcation

Artificial neural networks can harness stochasticity in multiple ways to enable a vast class of computationally powerful models. Electronic implementation of such stochastic networks is currently limited to addition of algorithmic noise to digital machines which is inherently inefficient; albeit recent efforts to harness physical noise in devices for stochasticity have shown promise. To succeed in fabricating electronic neuromorphic networks we need experimental evidence of devices with measurable and controllable stochasticity which is complemented with the development of reliable statistical models of such observed stochasticity. Current research literature has sparse evidence of the former and a complete lack of the latter. This motivates the current article where we demonstrate a stochastic neuron using an insulator-metal-transition (IMT) device, based on electrically induced phase-transition, in series with a tunable resistance. We show that an IMT neuron has dynamics similar to a piecewise linear FitzHugh-Nagumo (FHN) neuron and incorporates all characteristics of a spiking neuron in the device phenomena. We experimentally demonstrate spontaneous stochastic spiking along with electrically controllable firing probabilities using Vanadium Dioxide (VO$_2$) based IMT neurons which show a sigmoid-like transfer function. The stochastic spiking is explained by two noise sources - thermal noise and threshold fluctuations, which act as precursors of bifurcation. As such, the IMT neuron is modeled as an Ornstein-Uhlenbeck (OU) process with a fluctuating boundary resulting in transfer curves that closely match experiments. As one of the first comprehensive studies of a stochastic neuron hardware and its statistical properties, this article would enable efficient implementation of a large class of neuro-mimetic networks and algorithms.Comment: Added sectioning, Figure 6, Table 1, and Section II.E Updated abstract, discussion and corrected typo

When Substance Use Is Underreported: Comparing Self-Reports and Hair Toxicology in an Urban Cohort of Young Adults

Objective: Large-scale epidemiological research often uses self-reports to determine the prevalence of illicit substance use. Self-reports may suffer from inaccurate reporting but can be verified with objective measures. This study examined the following: the prevalence of illicit and non-medical substance use with self-reports and hair toxicology, the convergence of self-reported and objectively quantified substance use, and the correlates of under- and overreporting. Method: The data came from a large urban cohort study of young adults (n = 1,002, mean age = 20.6 years, 50% female). The participants provided 3 cm of hair (covering the previous 3 months) and reported their illicit and non-medical substance use and their sociodemographic, psychological, and behavioral characteristics. Hair toxicology analyses targeted cannabinoids, ketamine, opiates/opioids, stimulants including 3,4-methylenedioxymethamphetamine, and relevant metabolites. Results: Self-reports underestimated the prevalence of most substances by 30% to 60% compared to hair tests. The average detection ratio (hair test/self-report) was 1.50. Hair tests were typically more sensitive than self-reports. Underreporting was associated with a low level of that substance in hair. Self-reported delinquency and psychopathology were correlated with an increased likelihood of concordant positive self-reports and hair tests compared to underreporting. Overreporting was associated with infrequent self-reported use. Conclusion: Our study suggests that self-reports underestimate young adults' exposure to illicit substances and non-medical use of prescription drugs. Consequently, estimates of associations between substance use and risk factors or outcomes are likely biased. Combining self-reports with hair tests may be most beneficial in study samples with occasional substance use. Researchers can use specific factors (eg, detection ratios) to adjust prevalence estimates and correlations based on self-reports

When Substance Use Is Underreported: Comparing Self-Reports and Hair Toxicology in an Urban Cohort of Young Adults

Objective: Large-scale epidemiological research often uses self-reports to determine the prevalence of illicit substance use. Self-reports may suffer from inaccurate reporting but can be verified with objective measures. This study examined the following: the prevalence of illicit and non-medical substance use with self-reports and hair toxicology, the convergence of self-reported and objectively quantified substance use, and the correlates of under- and overreporting. Method: The data came from a large urban cohort study of young adults (n = 1,002, mean age = 20.6 years, 50% female). The participants provided 3 cm of hair (covering the previous 3 months) and reported their illicit and non-medical substance use and their sociodemographic, psychological, and behavioral characteristics. Hair toxicology analyses targeted cannabinoids, ketamine, opiates/opioids, stimulants including 3,4-methylenedioxymethamphetamine, and relevant metabolites. Results: Self-reports underestimated the prevalence of most substances by 30% to 60% compared to hair tests. The average detection ratio (hair test/self-report) was 1.50. Hair tests were typically more sensitive than self-reports. Underreporting was associated with a low level of that substance in hair. Self-reported delinquency and psychopathology were correlated with an increased likelihood of concordant positive self-reports and hair tests compared to underreporting. Overreporting was associated with infrequent self-reported use. Conclusion: Our study suggests that self-reports underestimate young adults' exposure to illicit substances and non-medical use of prescription drugs. Consequently, estimates of associations between substance use and risk factors or outcomes are likely biased. Combining self-reports with hair tests may be most beneficial in study samples with occasional substance use. Researchers can use specific factors (eg, detection ratios) to adjust prevalence estimates and correlations based on self-reports

Artificial intelligence-based detection of pneumonia in chest radiographs

Artificial intelligence is gaining increasing relevance in the field of radiology. This study retrospectively evaluates how a commercially available deep learning algorithm can detect pneumonia in chest radiographs (CR) in emergency departments. The chest radiographs of 948 patients with dyspnea between 3 February and 8 May 2020, as well as 15 October and 15 December 2020, were used. A deep learning algorithm was used to identify opacifications associated with pneumonia, and the performance was evaluated by using ROC analysis, sensitivity, specificity, PPV and NPV. Two radiologists assessed all enrolled images for pulmonal infection patterns as the reference standard. If consolidations or opacifications were present, the radiologists classified the pulmonal findings regarding a possible COVID-19 infection because of the ongoing pandemic. The AUROC value of the deep learning algorithm reached 0.923 when detecting pneumonia in chest radiographs with a sensitivity of 95.4%, specificity of 66.0%, PPV of 80.2% and NPV of 90.8%. The detection of COVID-19 pneumonia in CR by radiologists was achieved with a sensitivity of 50.6% and a specificity of 73%. The deep learning algorithm proved to be an excellent tool for detecting pneumonia in chest radiographs. Thus, the assessment of suspicious chest radiographs can be purposefully supported, shortening the turnaround time for reporting relevant findings and aiding early triage

Photoluminescent And Self-Assembled Hyaluronic Acid-Zinc Oxide-Ginsenoside Rh2 Nanoparticles And Their Potential Caspase-9 Apoptotic Mechanism Towards Cancer Cell Lines

Background: Zinc oxide nanoparticles (ZnO NPs) are used in modern cancer therapy based on their specific target, efficacy, low toxicity and biocompatibility. The photocatalytic performance of Zinc oxide (ZnO) nanocomposites with hyaluronic acid (HA) was used to study anticancer properties against various human cancer cell lines. Methods: Zinc oxide (ZnO) nanocomposites functionalized by hyaluronic acid (HA) were prepared by a co-precipitation method (HA-ZnONcs). The submicron-flower-shaped nanocomposites were further functionalized with ginsenoside Rh2 by a cleavable ester bond via carbodiimide chemistry to form Rh2HAZnO. The physicochemical behaviors of the synthesized ZnO nanocomposites were characterized by various analytical and spectroscopic techniques. We carried out 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyl tetrazolium bromide (MTT) assay to evaluate the toxicity of Rh2HAZnO in various human cancer cells (A549, MCF-7, and HT29). Furthermore, to confirm the apoptotic effects of Rh2HAZnO and to determine the role of the Caspase-9/p38 MAPK pathways by various molecular techniques such as RT-PCR and Western blotting. Furthermore, Rh2HAZnO induced morphological changes of these cell lines, mainly intracellular reactive oxygen species (ROS) were observed by ROS staining and nucleus by Hoechst staining. Results: We confirmed that Rh2HAZnO exhibits the anti-cancer effects on A549 lung cancer, HT29 colon cancer, and MCF7 breast cancer cells. Moreover, intracellular reactive oxygen species (ROS) were observed in three cancer cell lines. Rh2HAZnO induced apoptotic process through p53-mediated pathway by upregulating p53 and BAX and downregulating BCL2. Specifically, Rh2HAZnO induced activation of cleaved PARP (Asp214) in A549 lung cancer cells and upregulated Caspase-9/phosphorylation of p38 MAPK in other cell lines (HT29 and MCF-7). Furthermore, Rh2HAZnO induced morphological changes in the nucleus of these cell lines. Conclusion: These results suggest that the potential anticancer activity of novel Rh2HAZnO nanoparticles might be linked to induction of apoptosis through the generation of ROS by activation of the Caspase-9/p38 MAPK pathway

Associations of different hormonal contraceptive methods with hair concentrations of cortisol, cortisone, and testosterone in young women

Hair concentrations of cortisol, cortisone, and testosterone are non-invasive measures of cumulative steroid hormone levels. Use of contraceptives co-varies with levels of cortisol and cortisone in women's hair. It is unclear, however, how different contraceptive methods (i.e., that differ in their steroid hormone composition) affect corticosteroid and testosterone hair levels. The current study examines associations of contraceptives with hair steroid hormone concentrations in females from the community (N = 464, M = 20.6 years old, age range = 19-22). Self-reported contraceptives were first categorized as combined estrogen-progestin or progestin-only, and then analyzed individually in follow-up analyses. Multiple regressions adjusting for body mass index (BMI) and hair characteristics revealed that levels of hair cortisol, cortisone, and testosterone were significantly lower in women who used combined estrogen-progestin methods than in women who did not use hormonal contraception (βcortisol(log) = -0.29; βcortisone(log) = -0.28; βtestosterone(log) = -0.36), showing moderate to large effect sizes (d = 0.64, d = 0.71, and d = 0.81, respectively). Concentrations of hair cortisol were lower in women who used progestin-only contraceptives (β = -0.49) compared to no contraceptive use, with a large effect size (d = 1.67). Follow-up analyses revealed that the association of the three steroid hormones with estrogen-progestin methods was strongest for the combined oral "micro-pill." Future studies of hair steroid hormones should take into account the specific type of contraceptive used, as this may affect study results

Associations of psychoactive substances and steroid hormones in hair: Findings relevant to stress research from a large cohort of young adults

Objective: Epidemiological studies increasingly use hair samples to assess people’s cumulative exposure to steroid hormones, but how the use of different psychoactive substances may affect steroid hormone levels in hair is, so far, largely unknown. The current study addresses this gap by establishing the substance exposure correlates of cortisol, cortisone, and testosterone in hair, while also accounting for a number of relevant covariates. Method: Data came from a large urban community-sample of young adults with a high prevalence of substance use (N = 1002, mean age=20.6 years, 50.2% female), who provided 3 cm of hair samples. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) quantified cortisol, cortisone, and testosterone, as well as delta-9-tetrahydrocannabinol (THC), 3,4-methylenedioxymethamphetamine (MDMA, “Ecstasy”), cocaine, several opioids, and their respective metabolites. Multiple linear regression models with covariates were used to predict steroid hormone levels from substance exposure in a four-step approach: In the full sample, low and high substance hair concentrations (median split) were first tested against no use for each substance individually (step 1) and for all substances together (step 2). Then, within the participants with any substance in hair only, the continuous hair concentration of each substance in pg/mg (step 3) and finally of all substances together, were regressed (step 4). Results: Low, high, and continuous levels of THC in hair were robustly associated with higher levels of cortisol (sig. in step 1 low THC: β = 0.29, p = .021; high THC: β = 0.42, p = .001; step 2: low THC: β = 0.27, p = 0.036, and high THC: β = 0.40, p = .004, and step 4: β = 0.12, p = .041). Participants with high MDMA levels had higher levels of cortisone without adjusting for other substances (step 1: β = 0.34, p = .026), but this effect was not significant in the other models. While high THC levels were associated with lower levels of testosterone in step 2 (β = -0.35, p = .018), MDMA concentration was positively related to testosterone concentration with and without adjusting for other substances (step 3: β = 0.24, p = .041; step 4: β = 0.17, 95%, p = .015) in male participants. Conclusion: The use of psychoactive substances, especially of cannabis and ecstasy, should be considered in studies investigating steroid hormones in hair

The functional connectome of 3,4‐methyldioxymethamphetamine‐related declarative memory impairments

The chronic intake of 3,4‐methylenedioxymethamphetamine (MDMA, “ecstasy”) bears a strong risk for sustained declarative memory impairments. Although such memory deficits have been repeatedly reported, their neurofunctional origin remains elusive. Therefore, we here investigate the neuronal basis of altered declarative memory in recurrent MDMA users at the level of brain connectivity. We examined a group of 44 chronic MDMA users and 41 demographically matched controls. Declarative memory performance was assessed by the Rey Auditory Verbal Learning Test and a visual associative learning test. To uncover alterations in the whole brain connectome between groups, we employed a data‐driven multi‐voxel pattern analysis (MVPA) approach on participants' resting‐state functional magnetic resonance imaging data. Recent MDMA use was confirmed by hair analyses. MDMA users showed lower performance in delayed recall across tasks compared to well‐matched controls with moderate‐to‐strong effect sizes. MVPA revealed a large cluster located in the left postcentral gyrus of global connectivity differences between groups. Post hoc seed‐based connectivity analyses with this cluster unraveled hypoconnectivity to temporal areas belonging to the auditory network and hyperconnectivity to dorsal parietal regions belonging to the dorsal attention network in MDMA users. Seed‐based connectivity strength was associated with verbal memory performance in the whole sample as well as with MDMA intake patterns in the user group. Our findings suggest that functional underpinnings of MDMA‐related memory impairments encompass altered patterns of multimodal sensory integration within auditory processing regions to a functional heteromodal connector hub, the left postcentral gyrus. In addition, hyperconnectivity in regions of a cognitive control network might indicate compensation for degraded sensory processing

White matter alterations in chronic MDMA use: Evidence from diffusion tensor imaging and neurofilament light chain blood levels.

3,4-Methylenedioxymethamphetamine (MDMA, "Ecstasy") is a serotonin- and noradrenaline-releasing substance, currently among the most widely used illicit substances worldwide. In animal studies, repeated exposure to MDMA has been associated with dendritic but also axonal degeneration in the brain. However, translation of the axonal findings, specifically, to humans has been repeatedly questioned and the few existing studies investigating white matter alterations in human chronic MDMA users have yielded conflicting findings. In this study, we combined whole-brain diffusion tensor imaging and neurofilament light chain (NfL) analysis in blood to reveal potential MDMA-induced axonal neuropathology. To this end, we assessed 39 chronic MDMA users and 39 matched MDMA-naïve healthy controls. MDMA users showed increased fractional anisotropy in several white matter tracts, most prominently in the corpus callosum as well as corticospinal tracts, with these findings partly related to MDMA use intensity. However, the NfL levels of MDMA users were not significantly different from those of controls. We conclude that MDMA use is not associated with significant white matter lesions due to the absence of reduced fractional anisotropy and increased NfL levels commonly observed in conditions associated with white matter lesions, including stimulant and ketamine use disorders. Hence, the MDMA-induced axonal degradation demonstrated in animal models was not observed in this human study of chronic MDMA users

White matter alterations in chronic MDMA use: Evidence from diffusion tensor imaging and neurofilament light chain blood levels

3,4–Methylenedioxymethamphetamine (MDMA, “Ecstasy”) is a serotonin- and noradrenaline-releasing substance, currently among the most widely used illicit substances worldwide. In animal studies, repeated exposure to MDMA has been associated with dendritic but also axonal degeneration in the brain. However, translation of the axonal findings, specifically, to humans has been repeatedly questioned and the few existing studies investigating white matter alterations in human chronic MDMA users have yielded conflicting findings. In this study, we combined whole-brain diffusion tensor imaging and neurofilament light chain (NfL) analysis in blood to reveal potential MDMA-induced axonal neuropathology. To this end, we assessed 39 chronic MDMA users and 39 matched MDMA-naïve healthy controls. MDMA users showed increased fractional anisotropy in several white matter tracts, most prominently in the corpus callosum as well as corticospinal tracts, with these findings partly related to MDMA use intensity. However, the NfL levels of MDMA users were not significantly different from those of controls. We conclude that MDMA use is not associated with significant white matter lesions due to the absence of reduced fractional anisotropy and increased NfL levels commonly observed in conditions associated with white matter lesions, including stimulant and ketamine use disorders. Hence, the MDMA-induced axonal degradation demonstrated in animal models was not observed in this human study of chronic MDMA users