Assessment of serum calprotectin as a marker of inflammation in cattle with traumatic reticuloperitonitis.

Calprotectin (CP) is an inflammatory marker. The aim of the current study was to investigate oxidative stress and changes in CP in cattle with traumatic reticuloperitonitis (TRP). The study was divided into two groups, experimental (TRP) and healthy control group, with 10 animals in each group. Total leucocyte count, neutrophil and lymphocyte counts were higher in the TRP group compared to the control group and this increase was statistically significant (P < 0.001). The level of malondialdehyde (MDA) in TRP group was statistically significantly higher than the control group (P < 0.001). The level of glutathione (GSH) in the TRP group was statistically significantly lower than in the control group (P < 0.001). Serum Amyloid A (SAA) and CP values were higher in the TRP group and this difference was statistically significant (P < 0.001). It was concluded as a result of ROC analysis that CP, which has similar values with SAA, can be used diagnostically to confirm the inflammatory status in cattle with TRP.

Characterization and discrimination of spike protein in SARS-CoV-2 virus-like particles via surface-enhanced Raman spectroscopy.

Non-infectious virus-like particles (VLPs) are excellent structures for development of many biomedical applications such as drug delivery systems, vaccine production platforms, and detection techniques for infectious diseases including SARS-CoV-2 VLPs. The characterization of biochemical and biophysical properties of purified VLPs is crucial for development of detection methods and therapeutics. The presence of spike (S) protein in their structure is especially important since S protein induces immunological response. In this study, development of a rapid, low-cost, and easy-to-use technique for both characterization and detection of S protein in the two VLPs, which are SARS-CoV-2 VLPs and HIV-based VLPs was achieved using surface-enhanced Raman spectroscopy (SERS). To analyze and classify datasets of SERS spectra obtained from the VLP groups, machine learning classification techniques including support vector machine (SVM), k-nearest neighbors (kNN), and random forest (RF) were utilized. Among them, the SVM classification algorithm demonstrated the best classification performance for SARS-CoV-2 VLPs and HIV-based VLPs groups with 87.5% and 92.5% accuracy, respectively. This study could be valuable for the rapid characterization of VLPs for the development of novel therapeutics or detection of structural proteins of viruses leading to a variety of infectious diseases.

Insulin-like growth factor-1 expression levels in pro-inflammatory response in calves with neonatal systemic inflammatory response syndrome.

The objective of this study was to investigate the mRNA expression of insulin-like growth factor-1 (IGF-1), pro-inflammatory cytokines (IL-1ß, IL-6, IL-18, and TNF-α), serum immunoglobulin profiles (IgG and IgM), and lipid peroxidation status (MDA) in relation to pro-inflammatory cytokines. A case-controlled, prospective, and observational investigation was completed on 85 calves. Total RNA was isolated from whole blood samples of both the SIRS and healthy calves, followed by reverse transcription into cDNA. The resulting cDNAs were mixed with iTaq Universal SYBR Green Supermix and primers specific to the relevant genes using the Rotor-Gene Q instrument. After the reaction was completed, gene expressions were normalised against ß-actin using the 2-ΔΔCT method. The mRNA levels of pro-inflammatory cytokines namely (IL-1ß [SIRS: 2.15 ± 0.55, Control: 1.13 ± 0.62; P = 0.001], IL-6 [SIRS: 2.82 ± 0.52, Control: 0.91 ± 0.11; P < 0.001], IL-18 [SIRS: 1.92 ± 0.41, Control: 0.99 ± 0.13; P < 0.001], and TNF-α [SIRS: 2.59 ± 0.28, Control: 0.93 ± 0.09; P < 0.001]) and IGF-1 (SIRS: 3.55 ± 0.55, Control: 0.91 ± 0.15; P < 0.001) were up-regulated in calves with SIRS, while serum IgG (SIRS: 4.16 ± 0.26, Control: 1.73 ± 0.17; P < 0.001), IgM (SIRS: 1.55 ± 0.11, Control: 1.09 ± 0.13; P < 0.001), and MDA levels (SIRS: 41.12 ± 3.48, Control: 3.76 ± 0.81; P < 0.001) increased significantly in these calves. Furthermore, significant (P < 0.01) positive correlations were found in calves with SIRS in relation to the expression levels of IL-1ß, IL-6, IL-18, TNF-α, IGF-1, serum immunoglobulins, and MDA levels. These results suggest that IGF-1 could be a valuable pro-inflammatory marker, considering its high positive correlation with the expression levels of pro-inflammatory cytokines (IL-1ß, IL-6, IL-18, and TNF-α) and markers (MDA, IgG, and IgM) in calves with SIRS.

Investigation of Gold Nanoparticle Naproxen-Derived Conjugations in Ovarian Cancer.

Ovarian cancer, which is one of the most diagnosed cancer types among women, maintains its significance as a global health problem. Several drug candidates have been investigated for the potential treatment of ovarian cancer. Nonsteroidal anti-inflammatory drugs (NSAIDs) demonstrated anti-cancer activity through the inhibition of cyclooxygenase 2 (COX-2) and by inhibiting COX-2-dependent prostaglandin (PG) production. Naproxen is one of the most used NSAIDs and Naproxen-derived compounds (NDCs) may show potential treatment effects on cancer as chemotherapeutic drugs. Although there are successful drug development studies, the lack of solubility of these drug candidates in aqueous media results in limited bioavailability and high variability of patient responses during treatment. Low aqueous solubility is one of the main problems in the pharmaceutical industry in terms of drug development. Nanotechnology-based strategies provide solutions to hydrophobic drug limitations by increasing dispersion and improving internalization. In this study, two different NDCs (NDC-1 and NDC-2) bearing a thiosemicarbazide/1,2,4-triazole moiety were synthesized and tested for chemotherapeutic effects on ovarian cancer cells, which have a high COX-2 expression. To overcome the limited dispersion of these hydrophobic drugs, the drug molecules were conjugated to the surface of 13 nm AuNPs. Conjugation of drugs to AuNPs increased the distribution of drugs in aqueous media, and NDC@AuNP conjugates exhibited excellent colloidal stability for up to 8 weeks. The proposed system demonstrated an increased chemotherapeutic effect than the free drug counterparts with at least 5 times lower IC50 values. NDC@AuNP nanosystems induced higher apoptosis rates, which established a simple and novel way to investigate activity of prospective drugs in drug discovery research.

Investigation of haematological, inflammatory and immunological response in naturally infected cattle with Theileria annulata.

In this study, we aimed to investigate haematological, pro-inflammatory, inflammatory, anti-inflammatory and immunological responses in naturally Theileria annulata-infected cattle. The study material consisted of 25 Simmental cattle, 2-4 years of age, one of which was a control group consisting of healthy animals (Control group, n = 10), and the other was a Theileria group that include animals positive for Theileria annulata (Theileria group, n = 15). Haematological analysis (red blood cell [RBC], haemoglobin [HGB], haematocrit [HCT]), pro-inflammatory (tumour necrosis factor-α [TNF-α], nuclear factor kappa B [NF-ĸB] and interleukin-1 beta, [IL-1ß]), inflammatory (neutrophil-lymphocyte ratio [NLR]), anti-inflammatory (interleukin-10 [IL-10]) and antimicrobial peptide (CAMP) analyses were performed by using ELISA kit from blood samples. It was found that the rectal temperature of the Theileria group was found to be significantly higher (p < .001) than that of the control group. Haematological and biochemical analysis revealed that the RBC and HGB count and HCT percentage decreased (p < .001), while NF-ĸB (p < .001), TNF-α (p = .002), IL-1ß (p < .001), IL-10 (p = .012), NLR (p < .001) and CAMP (p = .037) levels increased in Theileria group compared to the control group. There was a strong correlation between NF-ĸB and TNF-α, NF-ĸB and IL-10, NLR and IL-1ß, NF-ĸB and CAMP, TNF-α and CAMP and IL-10 and CAMP. As a result of this study, it was revealed that a pro-inflammatory and immunological response also occurs along with the anti-inflammatory response in the inflammatory process.

Involuntary Movements in Cobalamin Deficiency.

OBJECTIVE: Neurologic problems are frequently described in infants with nutritional vitamin B12 (cobalamin) deficiency.Major neurologic consequences of infantile cobalamin deficiency include delays or regression in neurodevelopment and the occurrence of involuntary movements METHODS: We reviewed the medical records of infants with cobalamin deficiency and divided infants with involuntary movements into two groups as those, who developed involuntary movements during vitamin B12 supplementation (Group I) and those, who developed involuntary movements prior to supplementation therapy (Group II). RESULTS: We evaluated a total of 32 infants with the diagnosis of cobalamin deficiency. Involuntary movements were observed in 12 out of 32 infants. Group I and Group II consisted of 6 infants each. Of the infants with involuntary movements, five were exclusively breastfed until the time of diagnosis. The majority of infants in Group II had choreoathetoid movements; twitching and myoclonus in the face, tongue, and lips, and tremor in the upper extremities. These involuntary movements disappeared in one to three weeks after clonazepam therapy. In Group I; shaking movements, myoclonus, tremor, and twitching or protrusion were observed in patients' hands, feet, tongue, and lips on the 3rd-5th day of cobalamin supplementation. These involuntary movements disappeared within 5-12 days of clonazepam therapy. CONCLUSION: Recognition of nutritional cobalamin deficiency is important to perform a differential diagnosis of the condition from seizures or other causes of involuntary movements and avoid aggressive therapy and over treatment.

Overcome of Cisplatin Resistance in Ovarian Cancer by Combination of Low-intensity Ultrasound and Cisplatin.

BACKGROUND: Platinum-based chemotherapeutics are the main treatment options for ovarium cancer. However, the development of drug resistance is one of the major issues in chemotherapy. Even targeting a specific pathway may not produce an efficient sensitization to the chemo agent due to various pathways related to developing resistance against cisplatin. Hence, more effective strategies against drug resistance are essential for cancer treatment. In the presence of the ultrasonic waves, increased membrane porosity enables more drug uptake into the cancer cell. Therefore, the promising method in cancer therapy is seen as the use of the combined effect of chemotherapy and low-Intensity Ultrasound (LIUS) to overcome chemoresistance. OBJECTIVE: The present study aimed to examine the effects of Ultrasound (US)+Cisplatin (Cis)- based combination therapy to advance an effective treatment against drug resistance in cisplatinresistant ovarian cancer cells A2780 (A2780cis) under in vitro conditions. METHODS: Treatment groups as Cis and LIUS with two different ultrasound parameters (10% duty cycle (DC), 1 MHz, 1.0 W/cm2, 1 min and 50% DC, 1 MHz, 1.0 W/cm2, 3 min) were investigated to cope with the chemoresistance in A2780cis. After Cis therapy with IC50 concentrations determined in A2780 and A2780cis, drug-sensitive/resistant ovarian cancer cells were treated by ultrasound. The effects of the US+Cis combination therapy were evaluated by cell viability assays, colony formation, and cell cycle analyses. RESULTS: In both US parameters, the waveform was shown to be more effective in preventing drug resistance. While more than 80% of the cell population was suppressed with the US+Cis combination treatment, more than 90% of the colony formation was suppressed according to the colony formation test results. In addition, US+Cis combination therapy caused different cell cycle arrests in both A2780 and A2780cis cells. CONCLUSION: Ultrasound-based combination therapies show promising results in combating chemoresistance, and the disadvantages of conventional cancer treatments, such as cancer recurrence, drug resistance, and high cost, might reduce by the effectiveness of combination therapy of ultrasound therapy and chemotherapy.

Autoimmune Encephalitis with Autoantibodies to NMDAR1 following Herpes Encephalitis in Children and Adolescents.

Herpes simplex virus (HSV) type 1 is a frequent pathogen causing infectious encephalitis (HSVE). Early treatment with intravenous acyclovir has led to a significant decrease in mortality. However, especially in children, deterioration during or after HSVE may occur without any evidence of HSV reactivation or improvement following repeated antiviral therapy. Here, we report 15 patients (age range 3 months to 15 years) who suffered from autoimmune encephalitis with autoantibodies to NMDAR1 following Herpes encephalitis, presenting with movement abnormalities (young children) or neuropsychiatric symptoms (older children) as major complaints, respectively. The diagnosis was based on positive cerebrospinal fluid (CSF) and/or serum anti-NMDAR-antibodies with two children showing only positive CSF antibody findings. The time lag between first symptoms and diagnosis of autoimmune encephalitis was significantly longer than between first symptoms and diagnosis of HSVE (p <0.01). All patients improved during immunosuppressive treatment, during which plasmapheresis or rituximab treatments were applied in 11 patients, irrespective of their age. Despite immunotherapy, no patients relapsed with HSVE. Early diagnosis and treatment of autoimmune encephalitis after HSVE may be associated with a better outcome so that high clinical awareness and routine testing for anti-NMDAR-antibodies after HSVE seems advisable. If autoimmune encephalitis is suspected, antibody testing should also be performed on CSF if negative in serum.

Simple Staining of Cells on a Chip.

Simple staining of cells is a widely used method in basic medical diagnostics, education, and research laboratories. The stains are low-cost, but the extensive consumption results in excessive toxic waste generation. Thus, to decrease the amount of toxic waste resulting from the cell staining procedure is a need. In this study, we developed a magnetically driven and compartmentalized passive microfluidic chip to perform simple staining of human eukaryotic cells, K562 cells, and lymphocyte cells derived from patients. We demonstrated simple staining on cells with trypan blue, methylene blue, crystal violet, and safranin for high, medium, and low cell densities. The stained cells were imaged using a bright field optical microscope and a cell phone to count cells on the focal plane. The staining improved the color signal of the cell by 25-135-pixel intensity changes for the microscopic images. The validity of the protocol was determined using Jurkat and MDA-MB-231 cell lines as negative controls. In order to demonstrate the practicality of the system, lymphocyte cells derived from human blood samples were stained with trypan blue. The color intensity changes in the first and last compartments were analyzed to evaluate the performance of the chip. The developed method is ultra-low cost, significantly reduces the waste generated, and can be integrated with mobile imaging devices in terms of portability. By combining microfabrication technology with cell staining, this study reported a novel contribution to the field of microfluidic biosensors. In the future, we expect to demonstrate the detection of pathogens using this method.

Investigation of hemogram, oxidative stress, and some inflammatory marker levels in neonatal calves with escherichia coli and coronavirus diarrhea.

Calf diarrhea is the most common disease affecting calves in the neonatal period resulting in economic losses. Although predisposing factors play a role in the etiology of the disease, in most cases, different pathogens are involved in the development of the infection. In this study, hemogram data, glutathione and malondialdehyde levels were examined to determine lipid peroxidation and glutathione levels in E. coli- and coronavirus-infected calves. Serum amyloid A and calprotectin levels were also analyzed to determine inflammatory status. The study included a total of 45 female Montofon calves aged 0-1 week, including the E. coli group (15 calves), the coronavirus group (15 calves), and the control group (15 calves). Analysis revealed that total leukocyte, neutrophil, lymphocyte, malondialdehyde, serum amyloid A, and calprotectin levels increased in the coronavirus-infected calves compared with the E. coli group and the control group. In contrast, the levels of glutathione, one of the antioxidant markers, decreased. In conclusion, the main findings related to the determination of inflammation and oxidative status were characterized by the presence of E. coli and coronavirus diarrhea, and it is suggested that future studies may be guided by the fact that inflammatory conditions are higher in viral disease than in bacterial infection.

DeepCAN: A Modular Deep Learning System for Automated Cell Counting and Viability Analysis.

Precise and quick monitoring of key cytometric features such as cell count, size, morphology, and DNA content is crucial in life science applications. Traditionally, image cytometry relies on visual inspection of hemocytometers. This approach is error-prone due to operator subjectivity. Recently, deep learning approaches have emerged as powerful tools enabling quick and accurate image cytometry applicable to different cell types. Leading to simpler, compact, and affordable solutions, these approaches revealed image cytometry as a viable alternative to flow cytometry or Coulter counting. In this study, we demonstrate a modular deep learning system, DeepCAN, providing a complete solution for automated cell counting and viability analysis. DeepCAN employs three different neural network blocks called Parallel Segmenter, Cluster CNN, and Viability CNN that are trained for initial segmentation, cluster separation, and viability analysis. Parallel Segmenter and Cluster CNN blocks achieve accurate segmentation of individual cells while Viability CNN block performs viability classification. A modified U-Net network, a well-known deep neural network model for bioimage analysis, is used in Parallel Segmenter while LeNet-5 architecture and its modified version Opto-Net are used for Cluster CNN and Viability CNN, respectively. We train the Parallel Segmenter using 15 images of A2780 cells and 5 images of yeasts cells, containing, in total, 14742 individual cell images. Similarly, 6101 and 5900 A2780 cell images are employed for training Cluster CNN and Viability CNN models, respectively. 2514 individual A2780 cell images are used to test the overall segmentation performance of Parallel Segmenter combined with Cluster CNN, revealing high Precision/Recall/F1-Score values of 96.52%/96.45%/98.06%, respectively. Cell counting/viability performance of DeepCAN is tested with A2780 (2514 cells), A549 (601 cells), Colo (356 cells), and MDA-MB-231 (887 cells) cell images revealing high analysis accuracies of 96.76%/99.02%, 93.82%/95.93%, and 92.18%/97.90%, 85.32%/97.40%, respectively.

SERS-based sensor with a machine learning based effective feature extraction technique for fast detection of colistin-resistant Klebsiella pneumoniae.

Colistin-resistant Klebsiella pneumoniae (ColR-Kp) causes high mortality rates since colistin is used as the last-line antibiotic against multi-drug resistant Gram-negative bacteria. To reduce infections and mortality rates caused by ColR-Kp fast and reliable detection techniques are vital. In this study, we used a label-free surface-enhanced Raman scattering (SERS)-based sensor with machine learning algorithms to discriminate colistin-resistant and susceptible strains of K. pneumoniae. A total of 16 K. pneumoniae strains were incubated in tryptic soy broth (TSB) for 4 h. Collected SERS spectra of ColR-Kp and colistin susceptible K. pneumoniae (ColS-Kp) have shown some spectral differences that hard to discriminate by the naked eye. To extract discriminative features from the dataset, autoencoder and principal component analysis (PCA) that extract features in a non-linear and linear manner, respectively were performed. Extracted features were fed into the support vector machine (SVM) classifier to discriminate K. pneumoniae strains. Classifier performance was evaluated by using features extracted by each feature extraction techniques. Classification results of SVM classifier with extracted features by an autoencoder (autoencoder-SVM) has shown better performance than SVM classifier with extracted features by PCA (PCA-SVM). The accuracy, sensitivity, specificity, and area under curve (AUC) value of the autoencoder-SVM model were found as 94%, 94.2%, 93.8%, and 0.98, respectively. Furthermore, the autoencoder-SVM model has demonstrated statistically significantly better classifier performance than PCA-SVM in terms of accuracy and AUC values. These results illustrate that non-linear features can be more discriminative than linear ones to determine SERS spectral data of antibiotic-resistant and susceptible bacteria. Our methodological approach enables rapid and high accuracy detection of ColR-Kp and ColS-Kp, suggesting that this can be a promising tool to limit colistin resistance.

Highly Accurate Identification of Bacteria's Antibiotic Resistance Based on Raman Spectroscopy and U-Net Deep Learning Algorithms.

Bacterial pathogens especially antibiotic-resistant ones are a public health concern worldwide. To oppose the morbidity and mortality associated with them, it is critical to select an appropriate antibiotic by performing a rapid bacterial diagnosis. Using a combination of Raman spectroscopy and deep learning algorithms to identify bacteria is a rapid and reliable method. Nevertheless, due to the loss of information during training a model, some deep learning algorithms suffer from low accuracy. Herein, we modify the U-Net architecture to fit our purpose of classifying the one-dimensional Raman spectra. The proposed U-Net model provides highly accurate identification of the 30 isolates of bacteria and yeast, empiric treatment groups, and antimicrobial resistance, thanks to its capability to concatenate and copy important features from the encoder layers to the decoder layers, thereby decreasing the data loss. The accuracies of the model for the 30-isolate level, empiric treatment level, and antimicrobial resistance level tasks are 86.3, 97.84, and 95%, respectively. The proposed deep learning model has a high potential for not only bacterial identification but also for other diagnostic purposes in the biomedical field.

Effect of Chronic Valproic Acid Use on Anterior Cerebral Blood Flow of Children with Idiopathic Generalized Epilepsy.

OBJECTIVE: Cerebral blood flow has been blamed as a factor in the negative effect of antiepileptic drugs on neurocognition. This study aimed to investigate whether valproic acid (VPA), used for the treatment of idiopathic generalized epilepsy (IGE), causes a change in cerebral blood flow in children. METHODS: Included in this study were 33 children who were receiving VPA for IGE and 34 age-matched controls. Doppler and spectral measurements in common carotid artery (CCA), left and right internal CA (ICA) and external CA (ECA), anterior cerebral artery (ACA) and middle cerebral artery (MCA) were performed and the maximum velocity (VM), end-diastolic velocity (EDV), resistive index (RI), pulsatility index (PI) and flow rate (FR) were calculated. RESULTS: The mean age of drug and control groups were 9.33 plus or minus 2.11, and 9.74 plus or minus 2 years, respectively. Follow-up of patients was 17.7 plus or minus 3.2 months. The period of VPA treatment was 17.4 plus or minus 3.4 months. No statistically significant differences were found between control and VPA group for the VM, EDV, RI, PI, and FR values obtained from the bilateral ICA, ACA, and MCA. CONCLUSIONS: The results showed that VPA in therapeutic doses did not affect anterior cerebral blood flow. However according to result, it is still difficult to conclude that neurocognitive deterioration is not observed in patients receiving VPA.

Investigation of mammalian cells expressing SARS-CoV-2 proteins by surface-enhanced Raman scattering and multivariate analysis.

COVID-19 has caused millions of cases and deaths all over the world since late 2019. Rapid detection of the virus is crucial for controlling its spread through a population. COVID-19 is currently detected by nucleic acid-based tests and serological tests. However, these methods have limitations such as the requirement of high-cost reagents, false negative results and being time consuming. Surface-enhanced Raman scattering (SERS), which is a powerful technique that enhances the Raman signals of molecules using plasmonic nanostructures, can overcome these disadvantages. In this study, we developed a virus-infected cell model and analyzed this model by SERS combined with Principal Component Analysis (PCA). HEK293 cells were transfected with plasmids encoding the nucleocapsid (N), membrane (M) and envelope (E) proteins of SARS-CoV-2 via polyethyleneimine (PEI). Non-plasmid transfected HEK293 cells were used as the control group. Cellular uptake was optimized with green fluorescence protein (GFP) plasmids and evaluated by fluorescence microscopy and flow cytometry. The transfection efficiency was found to be around 60%. The expression of M, N, and E proteins was demonstrated by western blotting. The SERS spectra of the total proteins of transfected cells were obtained using a gold nanoparticle-based SERS substrate. Proteins of the transfected cells have peak positions at 646, 680, 713, 768, 780, 953, 1014, 1046, 1213, 1243, 1424, 2102, and 2124 cm-1. To reveal spectral differences between plasmid transfected cells and non-transfected control cells, PCA was applied to the spectra. The results demonstrated that SERS coupled with PCA might be a favorable and reliable way to develop a rapid, low-cost, and promising technique for the detection of COVID-19.

Determination of Optimum Ratio of Cationic Polymers and Small Interfering RNA with Agarose Gel Retardation Assay.

Nanomaterials have aroused attention in the recent years for their high potential for gene delivery applications. Most of the nanoformulations used in gene delivery are positively charged to carry negatively charged oligonucleotides. However, excessive positively charged carriers are cytotoxic. Therefore, the complexed oligonucleotide/nanoparticles should be well-examined before the application. In that manner, agarose gel electrophoresis, which is a basic method utilized for separation, identification, and purification of nucleic acid molecules because of its poriferous nature, is one of the strategies to determine the most efficient complexation rate. When the electric field is applied, RNA fragments can migrate through anode due to the negatively charged phosphate backbone. Because RNA has a uniform mass/charge ratio, RNA molecules run in agarose gel proportional according to their size and molecular weight. In this chapter, the determination of complexation efficiency between cationic polymer carriers and small interfering RNA (siRNA) cargos by using agarose gel electrophoresis is described. siRNA/cationic polymer carrier complexes are placed in an electric field and the charged molecules move through the counter-charged electrodes due to the phenomenon of electrostatic attraction. Nucleic acid cargos are loaded to cationic carriers via the electrostatic interaction between positively charged amine groups (N) of the carrier and negatively charged phosphate groups (P) of RNA. The N/P ratio determines the loading efficiency of the cationic polymer carrier. In here, the determination of N/P ratio, where the most efficient complexation occurs, by exposure to the electric field with a gel retardation assay is explained.

Triple-combination therapy assisted with ultrasound-active gold nanoparticles and ultrasound therapy against 3D cisplatin-resistant ovarian cancer model.

Cancer chemotherapy suffers from drug resistance and side effects of the drugs. Combination therapies have been attracted attention to overcome these limitations of traditional cancer treatments. Recently, increasing in intracellular chemotherapeutic concentration in the presence of ultrasonic waves (US) has been shown in the preclinical stage. In addition, some recent studies have shown that nanoparticles increase the effectiveness of ultrasound therapy. In this study, the US-active property of gold nanocones (AuNCs) was utilized for combinational US and cisplatin (Cis) to overcome drug resistance. The effect of the triple combination therapy US + AuNCs + Cis with low-dose Cis on 2/3D models of cisplatin-resistant ovarian cancer cell line (A2780cis) were investigated. In the 2D cell culture, 60% of the A2780cis cell population was suppressed with triple combination therapy; and the long-term therapeutic efficacy of the US + AuNCs + Cis with the low-dose drug was demonstrated by suppressing 83% of colony formation. According to the results in the 3D cell model, 60% of the spheroid formation was suppressed by the triple combination therapy with low-dose Cis. These results not only demonstrate the success of the US + AuNCs + Cis triple combination therapy for its long-term therapeutic effect on resistant cancer cells but also verified that it might enable effective cancer therapy in vivo and clinical stages based on the 3D tumor models. In addition, enhanced anti-cancer activity was demonstrated at the low-dose Cis on drug-resistant cancer cells indicating the triple-combination therapy successfully overcame drug resistance and this is a promising strategy to reduce the side effects of chemotherapy. This work exhibits a novel US and AuNCs-mediated combination cancer therapy, which demonstrates the role of ultrasound-active AuNCs to combat drug resistance with low-dose chemotherapy.

Discrimination of waterborne pathogens, Cryptosporidium parvum oocysts and bacteria using surface-enhanced Raman spectroscopy coupled with principal component analysis and hierarchical clustering.

Waterborne pathogens (parasites, bacteria) are serious threats to human health. Cryptosporidium parvum is one of the protozoan parasites that can contaminate drinking water and lead to diarrhea in animals and humans. Rapid and reliable detection of these kinds of waterborne pathogens is highly essential. Yet, current detection techniques are limited for waterborne pathogens and time-consuming and have some major drawbacks. Therefore, rapid screening methods would play an important role in controlling the outbreaks of these pathogens. Here, we used label-free surface-enhanced Raman Spectroscopy (SERS) combined with multivariate analysis for the detection of C. parvum oocysts along with bacterial contaminants including, Escherichia coli, and Staphylococcus aureus. Silver nanoparticles (AgNPs) are used as SERS substrate and samples were prepared with simply mixed of concentrated AgNPs with microorganisms. Each species presented distinct SERS spectra. Principal component analysis (PCA) and hierarchical clustering were performed to discriminate C. parvum oocysts, E. coli, and S. aureus. PCA was used to visualize the dataset and extract significant spectral features. According to score plots in 3 dimensional PCA space, species formed distinct group. Furthermore, each species formed different clusters in hierarchical clustering. Our study indicates that SERS combined with multivariate analysis techniques can be utilized for the detection of C. parvum oocysts quickly.

Drug-resistant Staphylococcus aureus bacteria detection by combining surface-enhanced Raman spectroscopy (SERS) and deep learning techniques.

Over the past year, the world's attention has focused on combating COVID-19 disease, but the other threat waiting at the door-antimicrobial resistance should not be forgotten. Although making the diagnosis rapidly and accurately is crucial in preventing antibiotic resistance development, bacterial identification techniques include some challenging processes. To address this challenge, we proposed a deep neural network (DNN) that can discriminate antibiotic-resistant bacteria using surface-enhanced Raman spectroscopy (SERS). Stacked autoencoder (SAE)-based DNN was used for the rapid identification of methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-sensitive S. aureus (MSSA) bacteria using a label-free SERS technique. The performance of the DNN was compared with traditional classifiers. Since the SERS technique provides high signal-to-noise ratio (SNR) data, some subtle differences were found between MRSA and MSSA in relative band intensities. SAE-based DNN can learn features from raw data and classify them with an accuracy of 97.66%. Moreover, the model discriminates bacteria with an area under curve (AUC) of 0.99. Compared to traditional classifiers, SAE-based DNN was found superior in accuracy and AUC values. The obtained results are also supported by statistical analysis. These results demonstrate that deep learning has great potential to characterize and detect antibiotic-resistant bacteria by using SERS spectral data.

Changes in sleep structure and sleep spindles are associated with the neuropsychiatric profile in paradoxical insomnia.

AIM: Sleep spindles have an important role in the pathophysiology and perception of sleep. We aimed to investigate the link between sleep spindles and microstructural architecture of sleep in regard to psychiatric characteristics in paradoxical insomnia. METHOD: A total of 40 participants (20 with paradoxical insomnia, 20 healthy controls) were included in the study. All participants were evaluated by somnologists and undergone a full-night polysomnography at sleep laboratory. In addition, psychiatric interview was made by the same psychiatrist, and questionnaires were performed to assess the dimensions of the personality such as the neuroticism or extroversion (Eysenck Personality Questionnaire, EPQR-A); to evaluate the tendency to exaggerate somatic perceptions (Somatosensory Amplification Scale, SSAS), somatic parts of dissociation (Somatoform Dissociation Questionnaire, SDQ-20), and somatization (Somatization Scale, SS); to measure participants' feelings about their health and disease anxiety (Health Anxiety Inventory, HAI-18), and the level of uncontrollable and persistent anxiety (Penn State Worry Questionnaire, PSWQ); to investigate the tendency to ruminative thinking (Ruminative Thought Style Questionnaire, RTSQ), alexithymia (Toronto Alexithymia Scale, TAS-20); and to define the presence and the severity of depressive symptoms (Beck Depression Inventory, BDI). RESULTS: The duration and frequency of the sleep spindles were similar between two groups, while the density was significantly decreased in paradoxical insomnia. The duration of sleep spindles, on the other hand, showed positive correlations with the extroversion dimension scores of EPQR-A and PSWQ scores. DISCUSSION: Sleep protective mechanisms are disturbed in paradoxical insomnia as shown by the lower density of sleep spindles. In addition, fast spindle activity is associated with the personality traits, characterized by an increase in the expression of feelings and the level of anxiety.