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The climate models are extremely complex pieces of software. They reflect best knowledge on physical components of the climate, nevertheless, they contain several parameters, which are too weakly constrained by observations, and can potentially lead to a crash of simulation. Recently a study by Lucas et al. (2013) has shown that machine learning methods can be used for predicting which combinations of parameters can lead to crash of simulation, and hence which processes described by these parameters need refined analyses. In the current study we reanalyse the dataset used in this research using different methodology. We confirm the main conclusion of the original study concerning suitability of machine learning for prediction of crashes. We show, that only three of the eight parameters indicated in the original study as relevant for prediction of the crash are indeed strongly relevant, three other are relevant but redundant, and two are not relevant at all. We also show that the variance due to split of data between training and validation sets has large influence both on accuracy of predictions and relative importance of variables, hence only cross-validated approach can deliver robust prediction of performance and relevance of variables. [ABSTRACT FROM AUTHOR]

Prostate-specific antigen (PSA) is the most commonly used marker of prostate cancer. However, nearly 25% of men with elevated PSA levels do not have cancer and nearly 20% of patients with prostate cancer have normal serum PSA levels. Therefore, in this study, Fourier transform infrared (FTIR) spectroscopy was investigated as a new tool for detection of prostate cancer from urine. Obtained results showed higher levels of glucose, urea and creatinine in urine collected from patients with prostate cancer than that in control. Principal component analysis (PCA) was not noticed possibility of differentiation urine collected from healthy and nonhealthy patients. However, machine learning algorithms showed 0.90 accuracy and precision of FTIR in detection of prostate cancer from urine. We showed that wavenumbers at 1614 cm -1 and 2972 cm -1 were candidates for prostate cancer spectroscopy markers. Importantly, these FTIR markers correlated with Gleason score, PSA and mpMRI PI-RADS category., (© 2024 Wiley‐VCH GmbH.)

The number of people suffering from type 2 diabetes has rapidly increased. Taking into account, that elevated intracellular lipid concentrations, as well as their metabolism, are correlated with diminished insulin sensitivity, in this study we would like to show lipids spectroscopy markers of diabetes. For this purpose, serum collected from rats (animal model of diabetes) was analyzed using Fourier Transformed Infrared-Attenuated Total Reflection (FTIR-ATR) spectroscopy. Analyzed spectra showed that rats with diabetes presented higher concentration of phospholipids and cholesterol in comparison with non-diabetic rats. Moreover, the analysis of second (II nd ) derivative spectra showed no structural changes in lipids. Machine learning methods showed higher accuracy for II nd derivative spectra (from 65 % to 89 %) than for absorbance FTIR spectra (53-65 %). Moreover, it was possible to identify significant wavelength intervals from II nd derivative spectra using random forest-based feature selection algorithm, which further increased the accuracy of the classification (up to 92 % for phospholipid region). Moreover decision tree based on the selected features showed, that peaks at 1016 cm -1 and 2936 cm -1 can be good candidates of lipids marker of diabetes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Atherosclerotic carotid stenosis (ACS) is a recognized risk factor for ischemic stroke. Currently, the gold diagnostic standard is Doppler ultrasound, the results of which do not provide certainty whether a given person should be qualified for surgery or not, because in some patients, carotid artery stenosis, for example at the level of 70 %, does not cause ischemic stroke in others yes. Therefore, there is a need for new methods that will clearly indicate the marker qualifying the patient for surgery. In this article we used Fourier Transform InfraRed Attenuated Total Reflectance (FTIR-ATR) spectra of serum collected from healthy and patients suffering from ACS, which had surgery were analyzed by machine learning and Principal Component Analysis (PCA) to determine chemical differences and spectroscopy marker of ACS. PCA demonstrated clearly differentiation between serum collected from healthy and non-healthy patients. Obtained results showed that in serum collected from ACS patients, higher absorbances of PO 2- stretching symmetric, CH 2 and CH 3 symmetric and asymmetric and amide I vibrations were noticed than in control group. Moreover, lack of peak at 1106 cm -1 was observed in spectrum of serum from non-control group. As a result of spectral shifts analysis was found that the most important role in distinguishing between healthy and unhealthy patients is played by FTIR ranges caused by vibrations of PO 2- phospholipids, amides III, II and CO lipid vibrations. Continuing, peaks at 1636 cm -1 and 2963 cm -1 were proposed as a potential spectroscopy markers of ACS. Finally, accuracy of obtained results higher than 90 % suggested, that FTIR-ATR can be used as an additional diagnostic tool in ACS qualifying for surgery., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Patients with high-grade ovarian cancer have a poor prognosis, thus effective treatment remains an unmet medical issue of high importance. Moreover, finding the reason for resistance to cisplatin is a crucial task for the improvement of anti-cancer drugs. In this study, we showed for the first time a chemical difference in a serum collected from platinum-resistance and platinum-sensitive women suffering from ovarian cancer using Fourier Transform InfraRed (FTIR) spectroscopy followed by a data analysis by Principal Component Analysis (PCA), Hierarchical Component Analysis (HCA) and 4 different machine learning algorithms. Obtained results showed a shift of PO 2 - symmetric vibrations, amide III and amide II were observed on the FTIR spectrum of the serum collected from platinum-resistance women in comparison with the spectrum of the serum from platinum-sensitive women. Furthermore, PCA analysis clearly demonstrated the most important role of amide II and amide I in the differentiation of platinum-sensitive and platinum-resistance women. In addition, machine learning algorithms showed the important role of wavenumber at 1631 cm -1 (amide I) and wavenumber at 2993 cm -1  (asymmetric stretching CH 3 vibrations). The accuracy of the obtained results was above 92%. Summarizing, FTIR can be used in detection platinum-resistance phenomena., (© 2024. The Author(s).)

Prostate cancer represents the second most prevalent form of cancer in males globally. In the diagnosis of prostate cancer, the most commonly utilised biomarker is prostate-specific antigen (PSA). It is unfortunate that approximately 25 % of men with elevated PSA levels do not have cancer, and that approximately 20 % of patients with prostate cancer have normal serum PSA levels. Accordingly, a more sensitive methodology must still be identified. It is imperative that new diagnostic methods should be non-invasive, cost-effective, rapid, and highly sensitive. Fourier transform infrared spectroscopy (FTIR) is a technique that fulfils all of the aforementioned criteria. Consequently, the present study used FTIR to assess dried serum samples obtained from a cohort of prostate cancer patients (n = 53) and a control group of healthy individuals (n = 40). Furthermore, this study proposes FTIR markers of prostate cancer obtained from serum. For this purpose, FTIR spectra of dried serum were measured and analysed using statistical, chemometric and machine learning (ML) algorithms including decision trees C5.0, Random Forest (RF), k-Nearest Neighbours (kNN) and Support Vector Machine (SVM). The FTIR spectra of serum collected from patients suffering from prostate cancer exhibited a reduced absorbance values of peaks derived from phospholipids, amides, and lipids. However, these differences were not statistically significant. Furthermore, principal component analysis (PCA) demonstrated that it is challenging to distinguish serum samples from healthy and non-healthy patients. The ML algorithms demonstrated that FTIR was capable of differentiating serum collected from both analysed groups of patients with high accuracy (values between 0.74 and 0.93 for the range from 800 cm -1 to 1800 cm -1 and around 0.70 and 1 for the range from 2800 cm -1 to 3000 cm -1 ), depending on the ML algorithms used. The results demonstrated that the peaks at 1637 cm -1 and 2851 cm -1 could serve as a FTIR marker for prostate cancer in serum samples. Furthermore, the correlation test indicated a clear correlation between these two wavenumbers and four of the five clinical parameters associated with prostate cancer. However, the relatively small number of samples collected only from patients over the age of 60 indicated that the results should be further investigated using a larger number of serum samples collected from a mean age range. In conclusion, this study demonstrated the potential of FTIR for the detection of prostate cancer in serum samples, highlighting the presence of distinctive spectroscopic markers associated with the analysed cancer type., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Atherosclerosis carotid stenosis (ACS) is one of the main causes of stroke. Unfortunately, the highest number of people go to the doctor with an advanced disease or as a result of a stroke, because carotid atherosclerosis does not cause obvious symptoms. Therefore, it is important to find a diagnostic method to detect the disease during routine tests (using blood or serum). Consequently, in this article, Raman spectroscopy was tested as a potential diagnostic method. Indeed, Raman spectra of serum collected from ACS and control patients showed decrease of Raman peak around 1520 cm -1 and increase of peak around 3050 cm -1 in people with ACS. Moreover in people with ACS shift of peaks originating from amides II, I and lipids vibrations were noticed in comparison with control group. Interestingly, decision tree algorithm showed that peaks at 1656 cm -1 and 2957 cm -1 could be a spectroscopy markers of atherosclerotic carotid stenosis. Continuing, Principal Component Analysis (PCA) clearly showed distinguishing between serum collected from ACS and control patients, while machine learning algorithms showed high value of accuracy, sensitivity and selectivity (more than 90 %). Finally, value of area under the curve of Receiver Operating Characteristic (AUC-ROC) showed value of 0.81 for Raman range between 800 cm -1 and 1800 cm -1 and 0.86 for 2800 cm -1 -3000 cm -1 range. Obtained results clearly showed possibility of Raman spectroscopy in detection of ACS from serum., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Platinum-resistant phenomena in ovarian cancer is very dangerous for women suffering from this disease, because reduces the chances of complete recovery. Unfortunately, until now there are no methods to verify whether a woman with ovarian cancer is platinum-resistant. Importantly, histopathology images also were not shown differences in the ovarian cancer between platinum-resistant and platinum-sensitive tissues. Therefore, in this study, Fourier Transform InfraRed (FTIR) and FT-Raman spectroscopy techniques were used to find chemical differences between platinum-resistant and platinum-sensitive ovarian cancer tissues. Furthermore, Principal Component Analysis (PCA) and machine learning methods were performed to show if it possible to differentiate these two kind of tissues as well as to propose spectroscopy marker of platinum-resistant. Indeed, obtained results showed, that in platinum-resistant ovarian cancer tissues higher amount of phospholipids, proteins and lipids were visible, however when the ratio between intensities of peaks at 1637 cm -1 (FTIR) and at 2944 cm -1 (Raman) and every peaks in spectra was calculated, difference between groups of samples were not noticed. Moreover, structural changes visible as a shift of peaks were noticed for C-O-C, C-H bending and amide II bonds. PCA clearly showed, that PC1 can be used to differentiate platinum-resistant and platinum-sensitive ovarian cancer tissues, while two-trace two-dimensional correlation spectra (2T2D-COS) showed, that only in amide II, amide I and asymmetric CH lipids vibrations correlation between two analyzed types of tissues were noticed. Finally, machine learning algorithms showed, that values of accuracy, sensitivity and specificity were near to 100% for FTIR and around 95% for FT-Raman spectroscopy. Using decision tree peaks at 1777 cm -1 , 2974 cm -1 (FTIR) and 1714 cm -1 , 2817 cm -1 (FT-Raman) were proposed as spectroscopy marker of platinum-resistant., (© 2024. The Author(s).)

Brain tumors are one of the most dangerous, because the position of these are in the organ that governs all life processes. Moreover, a lot of brain tumor types were observed, but only one main diagnostic method was used - histopathology, for which preparation of sample was long. Consequently, a new, quicker diagnostic method is needed. In this paper, FT-Raman spectra of brain tissues were analyzed by Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), four different machine learning (ML) algorithms to show possibility of differentiating between glioblastoma G4 and meningiomas, as well as two different types of meningiomas (atypical and angiomatous). Obtained results showed that in meningiomas additional peak around 1503 cm -1 and higher level of amides was noticed in comparison with glioblastoma G4. In the case of meningiomas differentiation, in angiomatous meningiomas tissues lower level of lipids and polysaccharides were visible than in atypical meningiomas. Moreover, PCA analyses showed higher distinction between glioblastoma G4 and meningiomas in the FT-Raman range between 800 cm -1 and 1800 cm -1 and between two types of meningiomas in the range between 2700 cm -1 and 3000 cm -1 . Decision trees showed, that the most important peaks to differentiate glioblastoma and meningiomas were at 1151 cm -1 and 2836 cm -1 while for angiomatous and atypical meningiomas - 1514 cm -1 and 2875 cm -1 . Furthermore, the accuracy of obtained results for glioblastoma G4 and meningiomas was 88 %, while for meningiomas - 92 %. Consequently, obtained data showed possibility of using FT-Raman spectroscopy in diagnosis of different types of brain tumors., (Copyright © 2024. Published by Elsevier Inc.)

The phenomenon of platinum resistance is a very serious problem in the treatment of ovarian cancer. Unfortunately, no molecular, genetic marker that could be used in assigning women suffering from ovarian cancer to the platinum-resistant or platinum-sensitive group has been discovered so far. Therefore, in this study, for the first time, we used FT-Raman spectroscopy to determine chemical differences and chemical markers presented in serum, which could be used to differentiate platinum-resistant and platinum-sensitive women. The result obtained showed that in the serum collected from platinum-resistant women, a significant increase of chemical compounds was observed in comparison with the serum collected from platinum-sensitive woman. Moreover, a decrease in the ratio between amides vibrations and shifts of peaks, respectively, corresponding to C-C/C-N stretching vibrations from proteins, amide III, amide II, C = O and CH lipids vibrations suggested that in these compounds, structural changes occurred. The Principal Component Analysis (PCA) showed that using FT-Raman range, where the above-mentioned functional groups were present, it was possible to differentiate the serum collected from both analyzed groups. Moreover, C5.0 decision tree clearly showed that Raman shifts at 1224 cm -1 and 2713 cm -1 could be used as a marker of platinum resistance. Importantly, machine learning methods showed that the accuracy, sensitivity and specificity of the FT-Raman spectroscopy were from 95 to 100%., (© 2023. The Author(s).)

Relevant attribute selection in machine learning is a key aspect aimed at simplifying the problem, reducing its dimensionality, and consequently accelerating computation. This paper proposes new algorithms for selecting relevant features and evaluating and selecting a subset of relevant objects in a dataset. Both algorithms are mainly based on the use of a fuzzy approach. The research presented here yielded preliminary results of a new approach to the problem of selecting relevant attributes and objects and selecting appropriate ranges of their values. Detailed results obtained on the Sonar dataset show the positive effects of this approach. Moreover, the observed results may suggest the effectiveness of the proposed method in terms of identifying a subset of truly relevant attributes from among those identified by traditional feature selection methods.

Background: Glioblastoma is among the most malignant brain cancer with an average survival rate measured in months. In neurosurgical practice, it is considered impossible to completely remove a glioblastoma because of difficulties in the intraoperative assessment of the boundaries between healthy brain tissue and glioblastoma cells. Therefore, it is important to find a new, quick, cost-effective and useful neurosurgical practice method for the intraoperative differentiation of glioblastoma from healthy brain tissue., Methods: Herein, the features of absorbance at specific wavenumbers considered characteristic of glioblastoma tissues could be markers of this cancer. We used Fourier transform infrared spectroscopy to measure the spectra of tissues collected from control and patients suffering from glioblastoma., Results: The spectrum obtained from glioblastoma tissues demonstrated an additional peak at 1612 cm -1 and a shift of peaks at 1675 cm -1 and 1637 cm -1 . Deconvolution of amide I vibrations showed that in the glioblastoma tissue, the percentage amount of β-sheet is around 20% higher than that in the control. Moreover, the principal component analysis showed that using fingerprint and amide I regions it is possible to distinguish cancer and non-cancer samples. Machine learning methods presented that the accuracy of the results is around 100%. Finally, analysis of the differences in the rate of change of Fourier transform infrared spectroscopy spectra showed that absorbance features between 1053 cm -1 and 1056 cm -1 as well as between 1564 cm -1 and 1588 cm -1 are characteristic of glioblastoma., Conclusion: Calculated features of absorbance at specific wavenumbers could be used as a spectroscopic marker of glioblastoma which may be useful in the future for neuronavigation., Competing Interests: Declaration of Competing Interest The authors declare no competing financial interests., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Background and Objective: Globally, gastric carcinoma (Gca) ranks fifth in terms of incidence and third in terms of mortality. Higher serum tumor markers (TMs) than those from healthy individuals, led to TMs clinical application as diagnostic biomarkers for Gca. Actually, there is no accurate blood test to diagnose Gca., Methods: Raman spectroscopy is applied as an efficient, credible, minimally invasive technique to evaluate the serum TMs levels in blood samples. After curative gastrectomy, serum TMs levels are important in predicting the recurrence of gastric cancer, which must be detected early. The experimentally assesed TMs levels using Raman measurements and ELİSA test were used to develop a prediction model based on machine learning techniques. A total of 70 participants diagnosed with gastric cancer after surgery (n = 26) and healthy (n = 44) were comrpised in this study., Results: In the Raman spectra of gastric cancer patients, an additional peak at 1182 cm -1 was observed and, the Raman intensity of amide III, II, I, and CH 2 proteins as well as lipids functional group was higher. Furthermore, Principal Component Analysis (PCA) showed, that it is possible to distinguish between the control and Gca groups using the Raman range between 800 and 1800 cm -1 , as well as between 2700 and 3000 cm -1 . The analysis of Raman spectra dynamics in gastric cancer and healthy patients showed, that the vibrations at 1302 and 1306 cm -1 were characteristic for cancer patients. In addition, the selected machine learning methods showed classification accuracy of more than 95%, while obtaining an AUROC of 0.98. Such results were obtained using Deep Neural Networks and the XGBoost algorithm., Conclusions: The obtained results suggest, that Raman shifts at 1302 and 1306 cm -1 could be spectroscopic markers of gastric cancer., Competing Interests: Declaration of Competing Interest All authors have read the journal's policy on disclosing potential conflicts of interest and declare that there is no conflict of interest. This study does not funded by any profit or non-profit agencies or none of organization has no role in the study conception/design, methods, data analysis or manuscript preparation., (Copyright © 2023 Elsevier B.V. All rights reserved.)

The presented article is focused on developing and validating an efficient, credible, minimally invasive technique based on spectral signatures of blood serum samples in patients with diagnosed recurrent pregnancy loss (RPL) versus healthy individuals who were followed at the Gynecology department. A total of 120 participants, RPL disease (n = 60) and healthy individuals (n = 60), participated in the study. First, we investigated the effect of circulating nerve growth factor (NGF) in RPL and healthy groups. To show NGF's effect, we measured the level of oxidative loads such as Total Antioxidant Level (TAS), Total Oxidant Level (TOS), and Oxidative Stress Index (OSI) with Beckman Coulter AU system and biochemical assays. We find a correlation between oxidative load and NGF level. Oxidative load mainly causes structural changes in the blood. Therefore, we obtained Raman measurements of the participant's serum. Then we selected two Raman regions, 800 and 1800 cm -1 , and between 2700 cm -1 and 3000 cm -1 , to see chemical changes. We noted that Raman spectra obtained for RPL and healthy women differed. The findings confirm that the imbalance between reactive oxygen species and antioxidants has important implications for the pathogenesis of RPL and that NGF levels accompany the level of oxidative load in the RPL state. Biomolecular structure and composition were determined using Raman spectroscopy and machine learning methods, and the correlation of these parameters was studied alongside machine learning technologies to advance toward clinical translation. Here we determined and validated the development of instrumentation for the Analysis of RPL patients' serum that can differentiate from control individuals with an accuracy of 100% using the Raman region corresponding to structural changes. Furthermore, this study found a correlation between traditional biochemical parameters and Raman data. This suggests that Raman spectroscopy is a sensitive tool for detecting biochemical changes in serum caused by RPL or other diseases., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Colorectal cancer is the second most common cause of cancer-related deaths worldwide. To follow up on the progression of the disease, tumor markers are commonly used. Here, we report serum analysis based on Raman spectroscopy to provide a rapid cancer diagnosis with tumor markers and two new cell adhesion molecules measured using the ELİSA method. Raman spectra showed higher Raman intensities at 1447 cm -1 1560 cm -1 , 1665 cm -1, and 1769 cm -1 , which originated from CH 2 proteins and lipids, amide II and amide I, and CO lipids vibrations. Furthermore, the correlation test showed, that only the CEA colon cancer marker correlated with the Raman spectra. Importantly, machine learning methods showed, that the accuracy of the Raman method in the detection of colon cancer was around 95 %. Obtained results suggest, that Raman shifts at 1302 cm -1 and 1306 cm -1 can be used as spectroscopy markers of colon cancer., Competing Interests: Declaration of competing interest The authors have no disclosures., (Copyright © 2023. Published by Elsevier Inc.)

The present article is focused on developing and validating an efficient, credible, minimally invasive technique based on spectral signatures of blood samples of women with recurrent miscarriage vs. those of healthy individuals who were followed in the Department of Obstetrics and Gynecology for 2 years. For this purpose, blood samples from a total of 120 participants, including healthy women (n=60) and women with diagnosed recurrent miscarriage (n=60), were obtained. The lipid profile (high-density lipoprotein, low-density lipoprotein, triglyceride, and total cholesterol levels) and lipid peroxidation (malondialdehyde and glutathione levels) were evaluated with a Beckman Coulter analyzer system for chemical analysis. Biomolecular structure and composition were determined using an attenuated total reflectance sampling methodology with Fourier transform infrared spectroscopy alongside machine learning technology to advance toward clinical translation. Here, we developed and validated instrumentation for the analysis of recurrent miscarriage patient serum that was able to differentiate recurrent miscarriage and control patients with an accuracy of 100% using a Fourier transform infrared region corresponding to lipids. We found that predictors of lipid profile abnormalities in maternal serum could significantly improve this patient pathway. The study also presents preliminary results from the first prospective clinical validation study of its kind., (© 2022. Springer-Verlag GmbH Germany, part of Springer Nature.)

Herein, we examined the modulatory effects ofApocynum (APO) on Monosodium Glutamate (MSG)-induced oxidative damage on the brain tissue of rats after long-term consumption of blood serum components by biochemical assays, Fourier transform infrared spectroscopy(FTIR), and machine learning methods. Sprague-Dawley male rats were randomly divided into the Control, Control + APO, MSG, and MSG + APO groups (n = 8 per group). All administrations were made by oral gavage saline, MSG, or APO and they were repeated for 28 days of the experiments. Brain tissue and blood serum samples were collected and analyzed for measurement levels ofmalondialdehyde (MDA),glutathione (GSH),myeloperoxidase (MPO), superoxide dismutase (SOD) activity, and Spectroscopic analysis. After 29 days, the results were evaluated using machine learning (ML). The levels of MDA and MPO showed changes in the MSG and MSG + APO groups, respectively. Changes in the proteins and lipids were observed in the FTIR spectra of the MSG groups. Additionally, APO in these animals improved the FTIR spectra to be similar to those in the Control group. The accuracy of the FTIR results calculated by ML was 100%. The findings of this study demonstrate that Apocynin treatment protectsagainst MSG-induced oxidative damage by inhibitingreactive oxygen speciesand upregulatingantioxidant capacity, indicating its potential in alleviatingthe toxic effects of MSG., (Copyright © 2022 Elsevier B.V. All rights reserved.)

In this research, blood samples of 47 patients infected by COVID were analyzed. The samples were taken on the 1st, 3rd and 6th month after the detection of COVID infection. Total antibody levels were measured against the SARS-CoV-2 N antigen and surrogate virus neutralization by serological methods. To differentiate COVID patients with different antibody levels, Fourier Transform InfraRed (FTIR) and Raman spectroscopy methods were used. The spectroscopy data were analyzed by multivariate analysis, machine learning and neural network methods. It was shown, that analysis of serum using the above-mentioned spectroscopy methods allows to differentiate antibody levels between 1 and 6 months via spectral biomarkers of amides II and I. Moreover, multivariate analysis showed, that using Raman spectroscopy in the range between 1317 cm -1 and 1432 cm -1 , 2840 cm -1 and 2956 cm -1 it is possible to distinguish patients after 1, 3, and 6 months from COVID with a sensitivity close to 100%., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 Elsevier Ltd. All rights reserved.)

The formation of the uterus lining, i.e. the endometrium, outside the uterus (ex. in the abdominal cavity,ovaries,or anywhere in the body) is called endometriosis. The presence of endometrial tissue present in the ovaries, thickens after menstruation, leading to menstrual-like bleeding and to the formation of chocolate cyst (Endometrioma) because of the accumulation of old, brown blood in the ovary. It is still unknown, what triggers the development ofendometrioma. However,it leads to excessive bleeding during menstrual periods or abnormal bleeding between periods and infertility. Endometriosis is often first diagnosed in those who seek medical attention for infertility. Therefore, new markers of endometrioma as well as new methods of its diagnosis are sought. In this study we used Raman spectra of serum collected from 50 healthy women and 50 women suffering from endometriosis. The obtained Raman data were used in multivariateanalysis to determine the Raman range, which can be used for endometriomadiagnostics. Partial Least Square (PLS), Principal Component Analysis (PCA) and Hierarchical Component Analysis (HCA) showed, that it is possible to distinguish between the serum collected from healthy and un-healthy women using the Raman range between 800 cm -1 and 1800 cm -1 and between 2956 cm -1 and 2840 cm -1 , while the first range corresponds to the fingerprint region and the second one to lipids vibrations. Consequently, the Pearson correlation test showeda significantpositive correlation betweenvaluesoflipidintensity in Raman spectra and volume of endometriomas. Summarizing, Raman spectroscopy can be a helpful tool in endometrioma diagnosis and the lipid vibrations are candidates for being a spectroscopic marker of the disease being studied., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

By in vitro fertilization, oocytes can be removed and the embryo can be cultured, and then trans cervically replaced when they reach cleavage or at the blastocyst stage. The characterization of the follicular fluid is important for the treatment process. Women who applied to the Academic Hospital in vitro fertilization (IVF) Center diagnosed with idiopathic female infertility (IFI) were sought in the patient group. Demographics and clinical gonadotropin measurements of the study population were recorded. Of the 116 follicular fluid samples (n=58 male-induced infertility; n=58 control) were analyzed using the FTIR system. To identify FTIR spectral characteristics of follicular fluids associated with an ovarian reserve and reproductive hormone levels from control and IFI, six machine learning methods and multivariate analysis were used. To assess the quantitative information about the total biochemical composition of a follicular fluid across various diagnoses. FTIR spectra showed a higher level of vibrations corresponding to lipids and a lower level of amide vibrations in the IFI group. Furthermore, the T square plot from Partial Last Square (PLS) analysis showed, that these vibrations can be used to distinguish IFI from the control group which was obtained by principal component analysis (PCA). Proteins and lipids play an important role in the development of IFI. The absorption dynamics of FTIR spectra showed wavenumbers with around 100% discrimination probability, which means, that the presented wavenumbers can be used as a spectroscopic marker of IFI. Also, six machine learning methods showed, that classification accuracy for the original set was from 93.75% to 100% depending on the learning algorithm used. These results can inform about IFI women's follicular fluid has biomacromolecular differentiation in their follicular fluid. By using a safe and effective tool for the characterization of changes in follicular fluid during in vitro fertilization, this study builds upon a comprehensive examination of the idiopathic female infertility remodeling process in human studies. We anticipate that this technology will be a valuable adjunct for clinical studies., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Polycystic ovarian syndrome (PCOS) is a disease, which causes infertility in women. The factors for the development of the disease are still not well understood and diagnostic methods need to be improved. Therefore, in this study, Raman spectroscopy as a potential diagnostic tool, was investigated and spectra of blood serum were collected from PCOS and healthy women. The obtained spectra showed distinct changes in intensities as well as shift of peaks for the blood serum collected from PCOS compared to healthy individuals. Partial Last Square (PLS) analysis and Principal Component Analysis (PCA) allowed to determine that Raman shifts of amides (1500 - 1700 cm -1 ) and CH 2 , CH 3 lipid groups (2700 - 3000 cm -1 ), could be thus used as potential PCOS markers. Furthermore, the Pearson correlation test showed a strong correlation between hormones (lutropin (LH), prolactin (PRL), follicle-stimulating (FSH), dehydroepiandrosterone (DHEAS), thyroid-stimulating (TSH), Estradiol) and lipids, as well as between hormones and protein functional groups in PCOS women, compared to the control. These results show, that the lipid and protein balance could be potentially applied as a helpful PCOS marker in Raman spectra., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

The study presented here considers the analysis of a medical dataset for the identification of the stage of onset of COVID-19 coronavirus. These data, presented in previous work by the authors, have been subjected to extensive analysis and additional calculations. The data were obtained by analyzing blood samples of infected individuals at 1, 3, and 6 months after COVID-19 infection. Results were obtained from FTIR spectrometry experiments. The results indicate a very effective ability to identify the different states of infection, and between 1 and 6 months even perfect. Specific spectrometry wavelength ranges can also be distinguished as medical markers., (© 2022 The Author(s). Published by Elsevier B.V.)

Stock price prediction is a popular yet challenging task and deep learning provides the means to conduct the mining for the different patterns that trigger its dynamic movement. In this paper, the task is to predict the close price for 25 companies enlisted at the Bucharest Stock Exchange, from a novel data set introduced herein. Towards this scope, two traditional deep learning architectures are designed in comparison: a long short-memory network and a temporal convolutional neural model. Based on their predictions, a trading strategy, whose decision to buy or sell depends on two different thresholds, is proposed. A hill climbing approach selects the optimal values for these parameters. The prediction of the two deep learning representatives used in the subsequent trading strategy leads to distinct facets of gain., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: Ruxandra Stoean is an Academic Editor of PLOS ONE. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Background: Transcriptional regulation in multi-cellular organisms is a complex process involving multiple modular regulatory elements for each gene. Building whole-genome models of transcriptional networks requires mapping all relevant enhancers and then linking them to target genes. Previous methods of enhancer identification based either on sequence information or on epigenetic marks have different limitations stemming from incompleteness of each of these datasets taken separately., Results: In this work we present a new approach for discovery of regulatory elements based on the combination of sequence motifs and epigenetic marks measured with ChIP-Seq. Our method uses supervised learning approaches to train a model describing the dependence of enhancer activity on sequence features and histone marks. Our results indicate that using combination of features provides superior results to previous approaches based on either one of the datasets. While histone modifications remain the dominant feature for accurate predictions, the models based on sequence motifs have advantages in their general applicability to different tissues. Additionally, we assess the relevance of different sequence motifs in prediction accuracy showing that even tissue-specific enhancer activity depends on multiple motifs., Conclusions: Based on our results, we conclude that it is worthwhile to include sequence motif data into computational approaches to active enhancer prediction and also that classifiers trained on a specific set of enhancers can generalize with significant accuracy beyond the training set.