Search

Abstract Background This study was conducted to address the existing drawbacks of inconvenience and high costs associated with sleep monitoring. In this research, we performed sleep staging using continuous photoplethysmography (PPG) signals for sleep monitoring with wearable devices. Furthermore, our aim was to develop a more efficient sleep monitoring method by considering both the interpretability and uncertainty of the model’s prediction results, with the goal of providing support to medical professionals in their decision-making process. Method The developed 4-class sleep staging model based on continuous PPG data incorporates several key components: a local attention module, an InceptionTime module, a time-distributed dense layer, a temporal convolutional network (TCN), and a 1D convolutional network (CNN). This model prioritizes both interpretability and uncertainty estimation in its prediction results. The local attention module is introduced to provide insights into the impact of each epoch within the continuous PPG data. It achieves this by leveraging the TCN structure. To quantify the uncertainty of prediction results and facilitate selective predictions, an energy score estimation is employed. By enhancing both the performance and interpretability of the model and taking into consideration the reliability of its predictions, we developed the InsightSleepNet for accurate sleep staging. Result InsightSleepNet was evaluated using three distinct datasets: MESA, CFS, and CAP. Initially, we assessed the model’s classification performance both before and after applying an energy score threshold. We observed a significant improvement in the model’s performance with the implementation of the energy score threshold. On the MESA dataset, prior to applying the energy score threshold, the accuracy was 84.2% with a Cohen’s kappa of 0.742 and weighted F1 score of 0.842. After implementing the energy score threshold, the accuracy increased to a range of 84.8–86.1%, Cohen’s kappa values ranged from 0.75 to 0.78 and weighted F1 scores ranged from 0.848 to 0.861. In the case of the CFS dataset, we also noted enhanced performance. Before the application of the energy score threshold, the accuracy stood at 80.6% with a Cohen’s kappa of 0.72 and weighted F1 score of 0.808. After thresholding, the accuracy improved to a range of 81.9–85.6%, Cohen’s kappa values ranged from 0.74 to 0.79 and weighted F1 scores ranged from 0.821 to 0.857. Similarly, on the CAP dataset, the initial accuracy was 80.6%, accompanied by a Cohen’s kappa of 0.73 and weighted F1 score was 0.805. Following the application of the threshold, the accuracy increased to a range of 81.4–84.3%, Cohen’s kappa values ranged from 0.74 to 0.79 and weighted F1 scores ranged from 0.813 to 0.842. Additionally, by interpreting the model’s predictions, we obtained results indicating a correlation between the peak of the PPG signal and sleep stage classification. Conclusion InsightSleepNet is a 4-class sleep staging model that utilizes continuous PPG data, serves the purpose of continuous sleep monitoring with wearable devices. Beyond its primary function, it might facilitate in-depth sleep analysis by medical professionals and empower them with interpretability for intervention-based predictions. This capability can also support well-informed clinical decision-making, providing valuable insights and serving as a reliable second opinion in medical settings.

Abstract Background One of the most common sleep disorders is sleep apnea syndrome. To diagnose sleep apnea syndrome, polysomnography is typically used, but it has limitations in terms of labor, cost, and time. Therefore, studies have been conducted to develop automated detection algorithms using limited biological signals that can be more easily diagnosed. However, the lack of information from limited signals can result in uncertainty from artificial intelligence judgments. Therefore, we performed selective prediction by using estimated respiratory signals from electrocardiogram and oxygen saturation signals based on confidence scores to classify only those sleep apnea occurrence samples with high confidence. In addition, for samples with high uncertainty, this algorithm rejected them, providing a second opinion to the clinician. Method Our developed model utilized polysomnography data from 994 subjects obtained from Massachusetts General Hospital. We performed feature extraction from the latent vector using the autoencoder. Then, one dimensional convolutional neural network—long short-term memory (1D CNN-LSTM) was designed and trained to measure confidence scores for input, with an additional selection function. We set a confidence score threshold called the target coverage and performed optimization only on samples with confidence scores higher than the target coverage. As a result, we demonstrated that the empirical coverage trained in the model converged to the target coverage. Result To confirm whether the model has been optimized according to the objectives, the coverage violation was used to measure the difference between the target coverage and the empirical coverage. As a result, the value of coverage violation was found to be an average of 0.067. Based on the model, we evaluated the classification performance of sleep apnea and confirmed that it achieved 90.26% accuracy, 91.29% sensitivity, and 89.21% specificity. This represents an improvement of approximately 7.03% in all metrics compared to the performance achieved without using a selective prediction. Conclusion This algorithm based on selective prediction utilizes confidence measurement method to minimize the problem caused by limited biological information. Based on this approach, this algorithm is applicable to wearable devices despite low signal quality and can be used as a simple detection method that determine the need for polysomnography or complement it.

BackgroundIn any health care system, both the classification of data and the confidence level of such classifications are important. Therefore, a selective prediction model is required to classify time series health data according to confidence levels of prediction. ObjectiveThis study aims to develop a method using long short-term memory (LSTM) models with a reject option for time series health data classification. MethodsAn existing selective prediction method was adopted to implement an option for rejecting a classification output in LSTM models. However, a conventional selection function approach to LSTM does not achieve acceptable performance during learning stages. To tackle this problem, we proposed a unit-wise batch standardization that attempts to normalize each hidden unit in LSTM to apply the structural characteristics of LSTM models that concern the selection function. ResultsThe ability of our method to approximate the target confidence level was compared by coverage violations for 2 time series of health data sets consisting of human activity and arrhythmia. For both data sets, our approach yielded lower average coverage violations (0.98% and 1.79% for each data set) than those of the conventional approach. In addition, the classification performance when using the reject option was compared with that of other normalization methods. Our method demonstrated superior performance for selective risk (12.63% and 17.82% for each data set), false-positive rates (2.09% and 5.8% for each data set), and false-negative rates (10.58% and 17.24% for each data set). ConclusionsOur normalization approach can help make selective predictions for time series health data. We expect this technique to enhance the confidence of users in classification systems and improve collaborative efforts between humans and artificial intelligence in the medical field through the use of classification that considers confidence.

Honey samples obtained by beekeepers taken from thirty-five different apiaries in Turkey’s region of Marmara were investigated for the presence of microorganism. Each honey sample was examined for the number of total aerobic mesofilic bacteria, coliforms, moulds and yeasts and the presence of Salmonella spp., Clostridium botulinum, along with other aerobic bacteriae such as Paenibacillus larvae and Melissococcus plutonius. In total, fifty-four honey samples of different botanical origins including unifloral (Umbelliferae, Leguminosae, Trifolium, Onobrychis), multifloral and chestnut were evaluated in the means of microbiological properties. Microorganisms were isolated in twenty-eight samples (60.86%) of pure cultures and eighteen samples (39.13%) of mixed cultures. On the other hand, no microorganisms were isolated in eight samples. Bacillus spp, Corynebacterium spp., Streptococcus spp., Staphylococcus spp. C. albicans and Penicillium spp. were isolated and identified in other honey samples. The bacteria counts were 4×102 - 1.4×103 cfu/g for aerobic mesofilic bacteria and 1-185 cfu/g for the fungi. The application of sanitary practices (hand washing, avoidance of sneezing or coughing, etc.) may be effective in controlling contamination by microorganisms. On the other hand, air, equipment and dust may be contributing causes of microbiological contamination. Therefore it is important to take precautions in order to avoid such contamination, even though such factors are often difficult to control.

Neuropathic pain is a chronic and sometimes intractable condition caused by lesions or diseases of the somatosensory nervous system. Many drugs are available but unfortunately do not provide satisfactory effects in patients, producing limited analgesia and undesirable side effects. Thus, there is an urgent need to develop new pharmaceutical agents to treat neuropathic pain. To date, highly specific agents that modulate a single target, such as receptors or ion channels, never progress to the clinic, which may reflect the diverse etiologies of neuropathic pain seen in the human patient population. Therefore, the development of multifunctional compounds exhibiting two or more pharmacological activities is an attractive strategy for addressing unmet medical needs for the treatment of neuropathic pain. To develop novel multifunctional compounds, key pharmacophores of currently used clinical pain drugs, including pregabalin, fluoxetine and serotonin analogs, were hybridized to the side chain of tianeptine, which has been used as an antidepressant. The biological activities of the hybrid analogs were evaluated at the human transporters of neurotransmitters, including serotonin (hSERT), norepinephrine (hNET) and dopamine (hDAT), as well as mu (μ) and kappa (κ) opioid receptors. The most advanced hybrid of these multifunctional compounds, 17, exhibited multiple transporter inhibitory activities for the uptake of neurotransmitters with IC50 values of 70 nM, 154 nM and 2.01 μM at hSERT, hNET and hDAT, respectively. Additionally, compound 17 showed partial agonism (EC50 = 384 nM) at the μ-opioid receptor with no influence at the κ-opioid receptor. In in vivo pain animal experiments, the multifunctional compound 17 showed significantly reduced allodynia in a spinal nerve ligation (SNL) model by intrathecal administration, indicating that multitargeted strategies in single therapy could considerably benefit patients with multifactorial diseases, such as pain.

M Aamir Ali, Marie L Borum Division of Gastroenterology and Liver Diseases, George Washington University, Washington, DC, USA Abstract: Up to 93% of patients with hereditary angioedema (HAE) experience recurrent abdominal pain. Many of these patients, who often present to emergency departments, primary care physicians, general surgeons, or gastroenterologists, are misdiagnosed for years and undergo unnecessary testing and surgical procedures. Making the diagnosis of HAE can be challenging because symptoms and attack locations are often inconsistent from one episode to the next. Abdominal attacks are common and can occur without other attack locations. An early, accurate diagnosis is central to managing HAE. Unexplained abdominal pain, particularly when accompanied by swelling of the face and extremities, suggests the diagnosis of HAE. A family history and radiologic imaging demonstrating edematous bowel also support an HAE diagnosis. Once HAE is suspected, C4 and C1 esterase inhibitor (C1-INH) laboratory studies are usually diagnostic. Patients with HAE may benefit from recently approved specific treatments, including plasma-derived C1-INH or recombinant C1-INH, a bradykinin B2-receptor antagonist, or a kallikrein inhibitor as first-line therapy and solvent/detergent-treated or fresh frozen plasma as second-line therapy for acute episodes. Short-term or long-term prophylaxis with nanofiltered C1-INH or attenuated androgens will prevent or reduce the frequency and severity of episodes. Gastroenterologists can play a critical role in identifying and treating patients with HAE, and should have a high index of suspicion when encountering patients with recurrent, unexplained bouts of abdominal pain. Given the high rate of abdominal attacks in HAE, it is important for gastroenterologists to appropriately diagnose and promptly recognize and treat HAE, or refer patients with HAE to an allergist. Keywords: hereditary angioedema, abdominal pain, diagnosis

Maryam Borumand, Sara Sibilla Minerva Research Labs Ltd., London, UK Abstract: With age, changes in the metabolic processes of structural components of the skin lead to visible signs of aging, such as increased dryness and wrinkle formation. The nutritional supplement, Pure Gold Collagen®, which consists of hydrolyzed collagen, hyaluronic acid, vitamins, and minerals, was developed to counteract these signs. An open-label study was conducted to investigate the effects of this nutritional supplement on skin properties. Supplementation with 50 mL of Pure Gold Collagen on a daily basis for 60 days led to a noticeable reduction in skin dryness, wrinkles, and nasolabial fold depth. In addition, a significant increase in collagen density and skin firmness was observed after 12 weeks. The data from this study suggest that Pure Gold Collagen can counteract signs of natural aging. Keywords: hydrolyzed collagen, antiaging, wrinkles, firmness, skin

While vaccines have been highly successful in protecting against various infections, there are still many high-priority pathogens for which there are no clinically approved formulations. To overcome this challenge, researchers have explored the use of nanoparticulate strategies for more effective antigen delivery to the immune system. Along these lines, nanotoxoids are a promising biomimetic platform that leverages cell membrane coating technology to safely deliver otherwise toxic bacterial antigens in their native form for antivirulence vaccination. Here, in order to further boost their immunogenicity, nanotoxoids formulated against staphylococcal α-hemolysin are embedded into a DNA-based hydrogel with immunostimulatory CpG motifs. The resulting nanoparticle-hydrogel composite is injectable and improves the in vivo delivery of vaccine antigens while simultaneously stimulating nearby immune cells. This leads to elevated antibody production and stronger antigen-specific cellular immune responses. In murine models of pneumonia and skin infection caused by methicillin-resistant Staphylococcus aureus, mice vaccinated with the hybrid vaccine formulation are well-protected. This work highlights the benefits of combining nanoparticulate antigen delivery systems with immunostimulatory hydrogels into a single platform, and the approach can be readily generalized to a wide range of infectious diseases.

EYA4 and GRHL2 encode transcription factors that play an important role in regulating many developmental stages. Since EYA4 and GRHL2 were identified as the transcription factors for the DFNA10 and DFNA28, 8 EYA4 mutations and 2 GRHL2 mutations have been reported worldwide. However, these genes have been reported in few studies of the Korean population. In this study, we performed a genetic analysis of EYA4 and GRHL2 in 87 unrelated Korean patients with autosomal dominant non-syndromic hearing loss (NSHL). A total of 4 genetic variants in the EYA4 gene were identified, including the 2 nonsense mutations p.S288X and p.Q393X. The novel mutation p.Q393X (c.1177C>T) resulted in a change in the codon at amino acid position 393 from a glutamine to a stop codon. The p.Q393X allele was predicted to encode a truncated protein lacking the entire C-terminal Eya homolog region (Eya HR), which is essential for the interaction with the transcription factor SIX3. The p.S288X (c.863C>A) mutation was found in a Korean family from a previous study. We analyzed p.S288X-linked microsatellite markers and determined that p.S288X might be a founder mutation and a hotspot mutation in Koreans. In GRHL2, a total of 4 genetic variants were identified, but none were associated with hearing loss in Korean patients. This suggests that GRHL2 may not be a main causal gene for autosomal dominant NSHL in Korean patients. In conclusion, our data provide fundamental information to predict the genotypes of Korean patients diagnosed with autosomal dominant NSHL.

Transmembrane protease serine 2 (TMPRSS2) is a plasma membrane protease that activates both spike protein of coronaviruses for cell entry and oncogenic signaling pathways for tumor progression. TMPRSS2 inhibition can reduce cancer invasion and metastasis and partially prevent the entry of SARS-CoV-2 into host cells. Thus, there is an urgent need for both TMPRSS2-selective imaging and precise screening of TMPRSS2 inhibitors. Here, we report a TMPRSS2-responsive surface-potential-tunable peptide-conjugated probe (EGTP) with aggregation-induced emission (AIE) features for TMPRSS2 selective imaging and accurate inhibitor screening. The amphiphilic EGTP was constructed with tunable surface potential and responsive efficiency with TMPRSS2 and its inhibitor. The rational construction of AIE luminogens (AIEgens) with modular peptides indicated that the cleavage of EGTP led to a gradual aggregation with bright fluorescence in high TMPRSS2-expressing cells. This strategy may have value for selective detection of cancer cells, SARS-CoV-2-target cells, and screening of protease inhibitors.

Tight junctions (TJs) are essential components of eukaryotic cells, and serve as paracellular barriers and zippers between adjacent tissues. TJs are critical for normal functioning of the organ of Corti, a part of the inner ear that causes loss of sensorineural hearing when damaged. To investigate the relation between genes involved in TJ function and hereditary loss of sensorineural hearing in the Korean population, we selected the TJP2 and CLDN14 genes as candidates for gene screening of 135 Korean individuals. The TJP2 gene, mutation of which causes autosomal dominant non-syndromic hearing loss (ADNSHL), lies at the DFNA51 locus on chromosome 9. The CLDN14 gene, mutation of which causes autosomal recessive non-syndromic hearing loss (ARNSHL), lies at the DFNB29 locus on chromosome 21. In the present study, we conducted genetic analyses of the TJP2 and CLDN14 genes in 87 unrelated patients with ADNSHL and 48 unrelated patients with either ARNSHL or potentially sporadic hearing loss. We identified two pathogenic variations, c.334G>A (p.A112T) and c.3562A>G (p.T1188A), and ten single nucleotide polymorphisms (SNPs) in the TJP2 gene. We found eight non-pathogenic variations in the CLDN14 gene. These findings indicate that, whereas mutation of the TJP2 gene might cause ADNSHL, CLDN14 is not a major causative gene for ARNSHL in the Korean population studied. Our findings may improve the understanding of the genetic cause of non-syndromic hearing loss in the Korean population.

Hearing loss (HL) is a congenital disease with a high prevalence, and patients with hearing loss need early diagnosis for treatment and prevention. The GJB2, MT-RNR1, and SLC26A4 genes have been reported as common causative genes of hearing loss in the Korean population and some mutations of these genes are the most common mutations associated with hearing loss. Accordingly, we developed a method for the simultaneous detection of seven mutations (c.235delC of GJB2, c.439A>G, c.919-2A>G, c.1149+3A>G, c.1229C>T, c.2168A>G of SLC26A4, and m.1555A>G of the MT-RNR1 gene) using multiplex SNaPshot minisequencing to enable rapid diagnosis of hereditary hearing loss. This method was confirmed in patients with hearing loss and used for genetic diagnosis of controls with normal hearing and neonates. We found that 4.06% of individuals with normal hearing and 4.32% of neonates were heterozygous carriers. In addition, we detected that an individual is heterozygous for two different mutations of GJB2 and SLC26A4 gene, respectively and one normal hearing showing the heteroplasmy of m.1555A>G. These genotypes corresponded to those determined by direct sequencing. Overall, we successfully developed a robust and cost-effective diagnosis method that detects common causative mutations of hearing loss in the Korean population. This method will be possible to detect up to 40% causative mutations associated with prelingual HL in the Korean population and serve as a useful genetic technique for diagnosis of hearing loss for patients, carriers, neonates, and fetuses.

DNA nanostructures are well-established vectors for packaging diversified payloads for targeted cellular delivery. Here, DNA origami rectangular sheets were combined with Herpes Simplex Virus 1 (HSV1) capsids to demonstrate surface coverage of the particle via electrostatic interactions. The optimized origami:HSV1 molar ratios led to characteristic packaging geometries ranging from dispersed HSV1 pockets to agglomerated HSV1 sleeves. Pockets were disguised from cells in HeLa and B16F10 cells and were 44.2% less infective than naked HSV1 particles. However, the pockets were 117% more infective than naked HSV1 particles when the origami sheets were coated with folic acid. We observed infectivity from naked origami, but they are 99.1% less infective with respect to HSV1 and 99.6% less infective with respect to the pocket complexes. This work suggests that DNA origami can selectively modulate virus infectivity.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a serious threat to human health and lacks an effective treatment. There is an urgent need for both real-time tracking and precise treatment of the SARS-CoV-2-infected cells to mitigate and ultimately prevent viral transmission. However, selective triggering and tracking of the therapeutic process in the infected cells remains challenging. Here, we report a main protease (Mpro)-responsive, mitochondrial-targeting, and modular-peptide-conjugated probe (PSGMR) for selective imaging and inhibition of SARS-CoV-2-infected cells via enzyme-instructed self-assembly and aggregation-induced emission (AIE) effect. The amphiphilic PSGMR was constructed with tunable structure and responsive efficiency and validated with recombinant proteins, cells transfected with Mpro plasmid or infected by SARS-CoV-2, and a Mpro inhibitor. By rational construction of AIE luminogen (AIEgen) with modular peptides and Mpro, we verified that the cleavage of PSGMR yielded gradual aggregation with bright fluorescence and enhanced cytotoxicity to induce mitochondrial interference of the infected cells. This strategy may have value for selective detection and treatment of SARS-CoV-2-infected cells.

Abstract Background The CyberKnife is an appealing delivery system for hypofractionated stereotactic body radiation therapy (SBRT) because of its ability to deliver highly conformal radiation therapy to moving targets. This conformity is achieved via 100s of non-coplanar radiation beams, which could potentially increase transitory testicular irradiation and result in post-therapy hypogonadism. We report on our early experience with CyberKnife SBRT for low- to intermediate-risk prostate cancer patients and assess the rate of inducing biochemical and clinical hypogonadism. Methods Twenty-six patients were treated with hypofractionated SBRT to a dose of 36.25 Gy in 5 fractions. All patients had histologically confirmed low- to intermediate-risk prostate adenocarcinoma (clinical stage ≤ T2b, Gleason score ≤ 7, PSA ≤ 20 ng/ml). PSA and total testosterone levels were obtained pre-treatment, 1 month post-treatment and every 3 months thereafter, for 1 year. Biochemical hypogonadism was defined as a total serum testosterone level below 8 nmol/L. Urinary and gastrointestinal toxicity was assessed using Common Toxicity Criteria v3; quality of life was assessed using the American Urological Association Symptom Score, Sexual Health Inventory for Men and Expanded Prostate Cancer Index Composite questionnaires. Results All 26 patients completed the treatment with a median 15 months (range, 13-19 months) follow-up. Median pre-treatment PSA was 5.75 ng/ml (range, 2.3-10.3 ng/ml), and a decrease to a median of 0.7 ng/ml (range, 0.2-1.8 ng/ml) was observed by one year post-treatment. The median pre-treatment total serum testosterone level was 13.81 nmol/L (range, 5.55 - 39.87 nmol/L). Post-treatment testosterone levels slowly decreased with the median value at one year follow-up of 10.53 nmol/L, significantly lower than the pre-treatment value (p < 0.013). The median absolute fall was 3.28 nmol/L and the median percent fall was 23.75%. There was no increase in biochemical hypogonadism at one year post-treatment. Average EPIC sexual and hormonal scores were not significantly changed by one year post-treatment. Conclusions Hypofractionated SBRT offers the radiobiological benefit of a large fraction size and is well-tolerated by men with low- to intermediate-risk prostate cancer. Early results are encouraging with an excellent biochemical response. The rate of new biochemical and clinical hypogonadism was low one year after treatment.