How to mitigate the risks of deployment of artificial intelligence in medicine?

The aim of this study is to examine the risks associated with the use of artificial intelligence (AI) in medicine and to offer policy suggestions to reduce these risks and optimize the benefits of AI technology. AI is a multifaceted technology. If harnessed effectively, it has the capacity to significantly impact the future of humanity in the field of health, as well as in several other areas. However, the rapid spread of this technology also raises significant ethical, legal, and social issues. This study examines the potential dangers of AI integration in medicine by reviewing current scientific work and exploring strategies to mitigate these risks. Biases in data sets for AI systems can lead to inequities in health care. Educational data that is narrowly represented based on a demographic group can lead to biased results from AI systems for those who do not belong to that group. In addition, the concepts of explainability and accountability in AI systems could create challenges for healthcare professionals in understanding and evaluating AI-generated diagnoses or treatment recommendations. This could jeopardize patient safety and lead to the selection of inappropriate treatments. Ensuring the security of personal health information will be critical as AI systems become more widespread. Therefore, improving patient privacy and security protocols for AI systems is imperative. The report offers suggestions for reducing the risks associated with the increasing use of AI systems in the medical sector. These include increasing AI literacy, implementing a participatory society-in-the-loop management strategy, and creating ongoing education and auditing systems. Integrating ethical principles and cultural values into the design of AI systems can help reduce healthcare disparities and improve patient care. Implementing these recommendations will ensure the efficient and equitable use of AI systems in medicine, improve the quality of healthcare services, and ensure patient safety.

Turkey's transition to face-to-face schooling during the COVID-19 pandemic.

BACKGROUND: The COVID-19 pandemic majorly disrupted conventional schooling and many countries maintained educational services through distance education. The duration of school closures in Turkey was longer than most OECD countries, thus Turkey prioritized school reopenings in the 2021-2022 academic year to mitigate possible negative outcomes of closures. Here we study the compatibility of implications for school reopenings in Turkey with these practices and assess the first semester of face-to-face schooling. METHODS: We have used document analysis to present and compare the practices in Turkey with international practices. We also used a comparative approach to assess the coherence between policies in Turkey and international suggestions. RESULTS: We find that vaccination rates of teachers and education staff are quite high in Turkey. Other practices, mandatory face masks, class-based closures and quarantine policies, are also in agreement with international practices. These steps are supported with frequent cleaning and ventilation of school environments, as well as with social distancing measures in schools. DISCUSSION: Consequently, the rate of daily closed classrooms has been kept below 1%, and the patterns of closures and openings are in general agreement with the changes of positive cases in the Turkish society. The net rate of closed classrooms decreased with the decline of quarantine days in Turkey. We hope that these insights will inform about school openings and contribute to best practices for face-to-face schooling.

Chimera states in hybrid coupled neuron populations.

Here we study the emergence of chimera states, a recently reported phenomenon referring to the coexistence of synchronized and unsynchronized dynamical units, in a population of Morris-Lecar neurons which are coupled by both electrical and chemical synapses, constituting a hybrid synaptic architecture, as in actual brain connectivity. This scheme consists of a nonlocal network where the nearest neighbor neurons are coupled by electrical synapses, while the synapses from more distant neurons are of the chemical type. We demonstrate that peculiar dynamical behaviors, including chimera state and traveling wave, exist in such a hybrid coupled neural system, and analyze how the relative abundance of chemical and electrical synapses affects the features of chimera and different synchrony states (i.e. incoherent, traveling wave and coherent) and the regions in the space of relevant parameters for their emergence. Additionally, we show that, when the relative population of chemical synapses increases further, a new intriguing chaotic dynamical behavior appears above the region for chimera states. This is characterized by the coexistence of two distinct synchronized states with different amplitude, and an unsynchronized state, that we denote as a chaotic amplitude chimera. We also discuss about the computational implications of such state.

Spike latency and jitter of neuronal membrane patches with stochastic Hodgkin-Huxley channels.

Ion channel stochasticity can influence the voltage dynamics of neuronal membrane, with stronger effects for smaller patches of membrane because of the correspondingly smaller number of channels. We examine this question with respect to first spike statistics in response to a periodic input of membrane patches including stochastic Hodgkin-Huxley channels, comparing these responses to spontaneous firing. Without noise, firing threshold of the model depends on frequency-a sinusoidal stimulus is subthreshold for low and high frequencies and suprathreshold for intermediate frequencies. When channel noise is added, a stimulus in the lower range of subthreshold frequencies can influence spike output, while high subthreshold frequencies remain subthreshold. Both input frequency and channel noise strength influence spike timing. Specifically, spike latency and jitter have distinct minima as a function of input frequency, showing a resonance like behavior. With either no input, or low frequency subthreshold input, or input in the low or high suprathreshold frequency range, channel noise reduces latency and jitter, with the strongest impact for the lowest input frequencies. In contrast, for an intermediate range of suprathreshold frequencies, where an optimal input gives a minimum latency, the noise effect reverses, and spike latency and jitter increase with channel noise. Thus, a resonant minimum of the spike response as a function of frequency becomes more pronounced with less noise. Spike latency and jitter also depend on the initial phase of the input, resulting in minimal latencies at an optimal phase, and depend on the membrane time constant, with a longer time constant broadening frequency tuning for minimal latency and jitter. Taken together, these results suggest how stochasticity of ion channels may influence spike timing and thus coding for neurons with functionally localized concentrations of channels, such as in "hot spots" of dendrites, spines or axons.

Effect of CYP4F2, VKORC1, and CYP2C9 in Influencing Coumarin Dose: A Single-Patient Data Meta-Analysis in More Than 15,000 Individuals.

The cytochrome P450 (CYP)4F2 gene is known to influence mean coumarin dose. The aim of the present study was to undertake a meta-analysis at the individual patients level to capture the possible effect of ethnicity, gene-gene interaction, or other drugs on the association and to verify if inclusion of CYP4F2*3 variant into dosing algorithms improves the prediction of mean coumarin dose. We asked the authors of our previous meta-analysis (30 articles) and of 38 new articles retrieved by a systematic review to send us individual patients' data. The final collection consists of 15,754 patients split into a derivation and validation cohort. The CYP4F2*3 polymorphism was consistently associated with an increase in mean coumarin dose (+9% (95% confidence interval (CI) 7-10%), with a higher effect in women, in patients taking acenocoumarol, and in white patients. The inclusion of the CYP4F2*3 in dosing algorithms slightly improved the prediction of stable coumarin dose. New pharmacogenetic equations potentially useful for clinical practice were derived.

Impact of synaptic noise and conductance state on spontaneous cortical firing.

Cortical neurons in-vivo operate in a continuum of overall conductance states, depending on the average level of background synaptic input throughout the dendritic tree. We compare how variability, or fluctuations, in this input affects the statistics of the resulting 'spontaneous' or 'background' firing activity, between two extremes of the mean input corresponding to a low-conductance (LC) and a high-conductance (HC) state. In the HC state, we show that both firing rate and regularity increase with increasing variability. In the LC state, firing rate also increases with input variability, but in contrast to the HC state, firing regularity first decreases and then increases with an increase in the variability. At high levels of input variability, firing regularity in both states converge to similar values.

Double inverse stochastic resonance with dynamic synapses.

We investigate the behavior of a model neuron that receives a biophysically realistic noisy postsynaptic current based on uncorrelated spiking activity from a large number of afferents. We show that, with static synapses, such noise can give rise to inverse stochastic resonance (ISR) as a function of the presynaptic firing rate. We compare this to the case with dynamic synapses that feature short-term synaptic plasticity and show that the interval of presynaptic firing rate over which ISR exists can be extended or diminished. We consider both short-term depression and facilitation. Interestingly, we find that a double inverse stochastic resonance (DISR), with two distinct wells centered at different presynaptic firing rates, can appear.

Autapse-induced multiple coherence resonance in single neurons and neuronal networks.

We study the effects of electrical and chemical autapse on the temporal coherence or firing regularity of single stochastic Hodgkin-Huxley neurons and scale-free neuronal networks. Also, we study the effects of chemical autapse on the occurrence of spatial synchronization in scale-free neuronal networks. Irrespective of the type of autapse, we observe autaptic time delay induced multiple coherence resonance for appropriately tuned autaptic conductance levels in single neurons. More precisely, we show that in the presence of an electrical autapse, there is an optimal intensity of channel noise inducing the multiple coherence resonance, whereas in the presence of chemical autapse the occurrence of multiple coherence resonance is less sensitive to the channel noise intensity. At the network level, we find autaptic time delay induced multiple coherence resonance and synchronization transitions, occurring at approximately the same delay lengths. We show that these two phenomena can arise only at a specific range of the coupling strength, and that they can be observed independently of the average degree of the network.

A Bayesian Estimation Framework for Pharmacogenomics Driven Warfarin Dosing: A Comparative Study.

The incorporation of pharmacogenomics information into the drug dosing estimation formulations has been shown to increase the accuracy in drug dosing and decrease the frequency of adverse drug effects in many studies in the literature. In this paper, an estimation framework based on the Bayesian structural equation modeling, which is driven by pharmacogenomics, is proposed. The results show that the model compares favorably with the linear models in terms of prediction and explaining the variations in warfarin dosing.

Activation kinetics of T-type calcium channel by a path probability approximation.

We previously formulated dynamics of ion channel gates by the path probability method. In this study, we apply that theoretical approach to derive the activation rate kinetics of T-type calcium channel in thalamic relay neurons. We derive explicit expressions of the forward and backward rate constants and show that the proposed rate constants accurately capture form of the empirical time constant, and that they also provide its saturation to a constant value at depolarized membrane potentials. We also compare our derivations with linear and nonlinear thermodynamic models of rate kinetics obtained from the same calcium channel, and show that it is possible to capture saturation of the time constant for the depolarized membrane potentials by the only proposed rate constants.

An improved non-linear thermodynamic model of voltage-dependent ionic currents.

Thermodynamic models of ionic currents are used to deduce exact functional form of rate constant. It is first assumed that free energy depends linearly on voltage. A major criticism of this approach is that time constant can reach arbitrarily small values resulting in an aberrant behavior. Recently, non-linear effects of electric field on the free energy were considered to solve this problem based on T-type calcium channel. In this study, we show that the current model approximately captures the voltage-dependence of the time constant only in a specific range of voltage and does not provide saturation of the time constant outside this range. Then, we propose an improved non-linear thermodynamic model and illustrate its applicability based on T-type calcium channel.

A new methodology to define the equilibrium value function in the kinetics of (in)activation gates.

A voltage-gated ion channel is fundamental in generation and propagation of electrical signals in the excitable membranes. Dynamics of (in)activation gates of the ion channel is modeled by first-order kinetics. The equilibrium value function is crucial in the kinetics of the (in)activation gates for fitting experimental data. We present a new methodology to define the equilibrium value function based on the lowest approximation of the cluster variation method and the static properties in the molecular field approximation. The methodology allows for exploration of the gating dynamics.

A new approach to define dynamics of the ion channel gates.

Voltage-gated ion channels are of great importance in the generation and propagation of electrical signals in the excitable cell membranes. How these channels respond to changes in the potential across the membrane has been a challenging problem, and different approaches have been proposed to address the mechanism of voltage sensing and gating in these channels. In this study, we attempt a new approach by considering a simple two-state gate system and applying the path probability method to construct a nonequilibrium statistical mechanical model of the system. The model which is based on the principles of statistical physics provides a firm physical basis for ion channel gating.

A computer software for simulating single-compartmental model of neurons.

In this paper, a new computer software package, Yalzer, is introduced for simulating single-compartmental model of neurons. Passive or excitable membranes with voltage-gated ion channels can be modeled, and current clamp and voltage clamp experiments can be simulated. In the Yalzer, first-order differential equations used to define the dynamics of the gate variables and the membrane potential are solved by two separate integration methods with variable time steps: forward Euler and exponential Euler methods. Outputs of the simulation are shown on a spreadsheet template for allowing flexible data manipulation and can be graphically displayed. The user can define the model in detail, and examine the excitability of the model and the dynamics of voltage-gated ion channels. The software package addresses to ones who want to run simple simulations of neurons without need to any programming language skills or expensive software. It can also be used for educational purposes.

Investigating the performance improvement of HRV Indices in CHF using feature selection methods based on backward elimination and statistical significance.

In this study, the best combination of short-term heart rate variability (HRV) measures was investigated to distinguish 29 patients with congestive heart failure from 54 healthy subjects in the control group. In the analysis performed, wavelet packet transform based frequency-domain measures and several non-linear parameters were used in addition to standard HRV measures. The backward elimination and unpaired statistical analysis methods were used to select the best one among all possible combinations of these measures. Five distinct typical classifiers with different parameters were evaluated in discriminating these two groups using the leave-one-out cross validation method. Each algorithm was tested 30 times to determine the repeatability of the results. The results imply that the backward elimination method gives better performance when compared to the statistical significance method in the feature selection stage. The best performance (82.75%, 96.29%, and 91.56% for the sensitivity, specificity, and accuracy) was obtained by using the SVM classifier with 27 selected features including non-linear and wavelet-based measures.

Dynamical structure underlying inverse stochastic resonance and its implications.

We investigate inverse stochastic resonance (ISR), a recently reported phenomenon in which the spiking activity of a Hodgkin-Huxley model neuron subject to external noise exhibits a pronounced minimum as the noise intensity increases. We clarify the mechanism that underlies ISR and show that its most surprising features are a consequence of the dynamical structure of the model. Furthermore, we show that the ISR effect depends strongly on the procedures used to measure it. Our results are important for the experimentalist who seeks to observe the ISR phenomenon.

Impact of genetic factors (CYP2C9, VKORC1 and CYP4F2) on warfarin dose requirement in the Turkish population.

Warfarin has a narrow therapeutic index and displays marked person-to-person variation in dose requirement. Functional polymorphisms at candidate genes can therefore offer utility as biomarkers to individualize warfarin treatment. The main objective of this study was to determine whether and to what extent variability in warfarin dose requirements was determined by polymorphisms in CYP2C9, VKORC1, CYP4F2 (rs2108622) and EPHX1 (rs2292566) in the Turkish population. Patients (n = 107) who had stable doses and international normalized ratio (INRs) at their last three consecutive visits were registered. Their demographic factors, concurrent medications, warfarin-related bleedings or thromboembolisms, smoking, alcohol intake and weekly green vegetable consumption were recorded. From a blood sample, DNA was isolated and genotyped by real-time PCR for polymorphisms of CYP2C9, VKORC1, CYP4F2 and EPHX1. A regression analysis was used to determine the independent effects of genetic and non-genetic factors on warfarin dose optimization. In our study, in addition to age, genetic variants of CYP2C9, VKORC1 and CYP4F2 were found to be significant predictor variables for the maintenance dose for warfarin, explaining 39.3% of dose variability. VKORC1 and CYP2C9 genotypes remain predictor variables of the warfarin dose, and we first found that CYP4F2 (rs2108622) contributes to dose variability in the Turkish population as well. These observations may be of benefit to future translation research with a view to global personalized medicine in regions hitherto understudied such as the Turkish population so as to rationalize initial warfarin dose and reduce the burden of frequent INR measurements.

The effect of rabeprazole on LES tone in experimental rat model.

INTRODUCTION: Despite adequate treatment with proton pump inhibitors (PPIs), symptoms of gastroesophageal reflux disease (GERD) may remain persistent as well as Barrett's esophagus may emerge. It may be proposed that the relaxant effect of PPIs on the smooth muscles may lead to resistance of symptoms. The aim of this study is to investigate effects of rabeprazole on the lower esophageal sphincter (LES) pressure with a rat model. MATERIALS AND METHODS: Sixteen rats were grouped as control and treatment groups. After obtaining LES tissues followed by a 60 min equilibration period for stabilization, contractile response to carbachol was obtained by application of single dose of carbachol to have a final concentration of 10(-6) M in the organ bath. After the contractions reached a plateau, concentration-response relationships for rabeprazole were obtained in a cumulative manner in the treatment group. RESULTS: In the carbachol contracted LES preparations; 1.5 × 10(-6) and 1.5×10(-5) M of rabeprazole caused 6.08% and 11.34% relaxations respectively which were not statistically significant. However, mean integral relaxation value for 4.5 × 10(-5) M of rabeprazole was 17.34% and this relaxation was significant compared with controls. CONCLUSIONS: In the present study, rabeprazole caused no direct significant change in LES tone in the therapeutic dose range applied to the organ bath. However, rabeprazole at the high dose caused a significant decrease in the LES tone.

Superiority of ceftriaxon to cefazolin in a rat model of obstructive jaundice: an experimental study.

OBJECTIVE: The objective of this study was to evaluate the serum and bile concentrations of cefazolin and ceftriaxone at the third and sixth hours in an experimental obstructive jaundice model and to identify the rate of excretion of these antibiotics into the bile. MATERIAL AND METHODS: Thirty-two Wistar albino rats were used in this study. The bile and serum levels of cefazolin were measured at the third hour in the A1 group and at the sixth hour in the A2 group, with cefazolin administered as 5 mg/rat; while the bile and serum levels of ceftriaxone were studied at the third hour in the B1 group and at the sixth hour in the B2 group, with ceftriaxone administered as 5 mg/rat. RESULTS: After 3 hr of cefazolin administration, the serum concentration in the A1 group reached a mean of 1.8 µg/ml, while the bile concentration was 90% of the serum concentration, with a mean of 1.6 µg/ml; whereas in the B1 group, the third-hour serum concentration of ceftriaxone was 18.6 µg/ml, while the bile concentration was found to be as high as 330% of this level, i.e., 56 µg/ml. The serum value of cefazolin decreased to 1.4 µg/ml in the A2 group and ceftriaxone decreased to 3.7 µg/ml in the B2 group at the sixth hour. CONCLUSIONS: Although the excretory level of cefazolin and ceftriaxone into the bile reaches therapeutic doses, the duration for which these levels are above those required for bactericidal activity is short. Ceftriaxone is better concentrated in the serum and bile than cefazolin.

Epileptic seizure detection using probability distribution based on equal frequency discretization.

In this study, we offered a new feature extraction approach called probability distribution based on equal frequency discretization (EFD) to be used in the detection of epileptic seizure from electroencephalogram (EEG) signals. Here, after EEG signals were discretized by using EFD method, the probability densities of the signals were computed according to the number of data points in each interval. Two different probability density functions were defined by means of the polynomial curve fitting for the subjects without epileptic seizure and the subjects with epileptic seizure, and then when using the mean square error criterion for these two functions, the success of epileptic seizure detection was 96.72%. In addition, when the probability densities of EEG segments were used as the inputs of a multilayer perceptron neural network (MLPNN) model, the success of epileptic seizure detection was 99.23%. This results show that non-linear classifiers can easily detect the epileptic seizures from EEG signals by means of probability distribution based on EFD.