The BioCascade Impactor: A novel device for direct collection of size-fractionated bioaerosols into liquid medium.

The ability to collect size-fractionated airborne particles that contain viable bacteria and fungi directly into liquid medium while also maintaining their viability is critical for assessing exposure risks. In this study, we present the BioCascade impactor, a novel device designed to collect airborne particles into liquid based on their aerodynamic diameter in three sequential stages (>9.74 µm, 3.94-9.74 µm, and 1.38-3.94 µm when operated at 8.5 L/min). Aerosol samples containing microorganisms - either Saccharomyces kudriavzevii or Micrococcus luteus, were used to evaluate the performance of the BioCascade (BC) paired with either the VIable Virus Aerosol Sampler (VIVAS) or a gelatin filter (GF) as stage 4 to collect particles <1.38 µm. Stages 2 and 3 collected the largest fractions of viable S. kudriavzevii when paired with VIVAS (0.468) and GF (0.519), respectively. Stage 3 collected the largest fraction of viable M. luteus particles in both BC+VIVAS (0.791) and BC+GF (0.950) configurations. The distribution function of viable microorganisms was consistent with the size distributions measured by the Aerodynamic Particle Sizer. Testing with both bioaerosol species confirmed no internal loss and no re-aerosolization occurred within the BC. Irrespective of the bioaerosol tested, stages 1, 3 and 4 maintained ≥80% of viability, while stage 2 maintained only 37% and 73% of viable S. kudriavzevii and M. luteus, respectively. The low viability that occurred in stage 2 warrants further investigation. Our work shows that the BC can efficiently size-classify and collect bioaerosols without re-aerosolization and effectively maintain the viability of collected microorganisms.

The BioCascade-VIVAS system for collection and delivery of virus-laden size-fractionated airborne particles.

The size of virus-laden particles determines whether aerosol or droplet transmission is dominant in the airborne transmission of pathogens. Determining dominant transmission pathways is critical to implementing effective exposure risk mitigation strategies. The aerobiology discipline greatly needs an air sampling system that can collect virus-laden airborne particles, separate them by particle diameter, and deliver them directly onto host cells without inactivating virus or killing cells. We report the use of a testing system that combines a BioAerosol Nebulizing Generator (BANG) to aerosolize Human coronavirus (HCoV)-OC43 (OC43) and an integrated air sampling system comprised of a BioCascade impactor (BC) and Viable Virus Aerosol Sampler (VIVAS), together referred to as BC-VIVAS, to deliver the aerosolized virus directly onto Vero E6 cells. Particles were collected into four stages according to their aerodynamic diameter (Stage 1: >9.43 µm, Stage 2: 3.81-9.43 µm, Stage 3: 1.41-3.81 µm and Stage 4: <1.41 µm). OC43 was detected by reverse-transcription quantitative polymerase chain reaction (RT-qPCR) analyses of samples from all BC-VIVAS stages. The calculated OC43 genome equivalent counts per cm3 of air ranged from 0.34±0.09 to 70.28±12.56, with the highest concentrations in stage 3 (1.41-3.81 µm) and stage 4 (<1.41 µm). Virus-induced cytopathic effects appeared only in cells exposed to particles collected in stages 3 and 4, demonstrating the presence of viable OC43 in particles <3.81 µm. This study demonstrates the dual utility of the BC-VIVAS as particle size-fractionating air sampler and a direct exposure system for aerosolized viruses. Such utility may help minimize conventional post-collection sample processing time required to assess the viability of airborne viruses and increase the understanding about transmission pathways for airborne pathogens.

Production of docosahexaenoic acid by a novel isolated Aurantiochytrium sp. 6-2 using fermented defatted soybean as a nitrogen source for sustainable fish feed development.

We focused on the production of DHA-containing microbial lipids by Aurantiochytrium sp. using of defatted soybean (DS) as a nitrogen source. DS is a plant biomass that could provide a sustainable supply at a low cost. Results showed that Aurantiochytrium sp. could not directly assimilate the DS as a nitrogen source but could grow well in a medium containing DS fermented with rice malt. When cultivated in a fermented DS (FDS) medium, Aurantiochytrium sp. showed vigorous growth with the addition of sufficient sulfate and chloride ions as inorganic nutrients without seawater salt. A novel isolated Aurantiochytrium sp. 6-2 showed 15.8 ± 3.4 g/L DHA productivity (in 54.8 ± 12.1 g/L total fatty acid production) in 1 L of the FDS medium. Therefore, DHA produced by Aurantiochytrium sp. using FDS enables a stable and sustainable DHA supply and could be an alternative source of natural DHA derived from fish oil.

Building Environmental and Sociological Predictive Intelligence to Understand the Seasonal Threat of SARS-CoV-2 in Human Populations.

Current modeling practices for environmental and sociological modulated infectious diseases remain inadequate to forecast the risk of outbreak(s) in human populations, partly due to a lack of integration of disciplinary knowledge, limited availability of disease surveillance datasets, and overreliance on compartmental epidemiological modeling methods. Harvesting data knowledge from virus transmission (aerosols) and detection (wastewater) of SARS-CoV-2, a heuristic score-based environmental predictive intelligence system was developed that calculates the risk of COVID-19 in the human population. Seasonal validation of the algorithm was uniquely associated with wastewater surveillance of the virus, providing a lead time of 7-14 days before a county-level outbreak. Using county-scale disease prevalence data from the United States, the algorithm could predict COVID-19 risk with an overall accuracy ranging between 81% and 98%. Similarly, using wastewater surveillance data from Illinois and Maryland, the SARS-CoV-2 detection rate was greater than 80% for 75% of the locations during the same time the risk was predicted to be high. Results suggest the importance of a holistic approach across disciplinary boundaries that can potentially allow anticipatory decision-making policies of saving lives and maximizing the use of available capacity and resources.

Distinct serum GDNF coupling with brain structural and functional changes underlies cognitive status in Parkinson's disease.

AIM: Aberrations in brain connections are implicated in the pathogenesis of Parkinson's disease (PD). We previously demonstrated that Glial cell-derived neurotrophic factor (GDNF) reduction is associated with cognition decline. Nonetheless, it is elusive if the pattern of brain topological connectivity differed across PD with divergent serum GDNF levels, and the accompanying profile of cognitive deficits has yet to be determined. METHODS: We collected data on the participants' cognition, demographics, and serum GDNF levels. Participants underwent 3.0T magnetic resonance imaging, and we assessed the degree centrality, brain network topology, and cortical thickness of the healthy control (HC) (n = 25), PD-high-GDNF (n = 19), and PD-low-GDNF (n = 19) groups using graph-theoretic measures of resting-state functional MRI to reveal how much brain connectivity varies and its clinical correlates, as well as to determine factors predicting the cognitive status in PD. RESULTS: The results show different network properties between groups. Degree centrality abnormalities were found in the right inferior frontal gyrus and right parietal lobe postcentral gyrus, linked with cognition scores. The two aberrant clusters serve as a potentially powerful signal for determining whether a patient has PD and the patient's cognition level after integrating with GDNF, duration, and dopamine dosage. Moreover, we found a significant positive relationship between the thickness of the left caudal middle frontal lobe and a plethora of cognitive domains. Further discriminant analysis revealed that the cortical thickness of this region could distinguish PD patients from healthy controls. The mental state evaluation will also be more precise when paired with GDNF and duration. CONCLUSION: Our findings reveal that the topological features of brain networks and cortical thickness are altered in PD patients with cognitive deficits. The above change, accompanied by the serum GDNF, may have merit as a diagnosis marker for PD and, arguably, cognition status.

Detection and isolation of infectious SARS-CoV-2 omicron subvariants collected from residential settings.

Airborne transmission of infectious (viable) SARS-CoV-2 is increasingly accepted as the primary manner by which the virus is spread from person to person. Risk of exposure to airborne virus is higher in enclosed and poorly ventilated spaces. We present a study focused on air sampling within residences occupied by individuals with COVID-19. Air samplers (BioSpot-VIVAS, VIVAS, and BC-251) were positioned in primary- and secondary-occupancy regions in seven homes. Swab samples were collected from high-touch surfaces. Isolation of SARS-CoV-2 was attempted for samples with virus detectable by RT-qPCR. Viable virus was quantified by plaque assay, and complete virus genome sequences were obtained for selected samples from each sampling day. SARS-CoV-2 was detected in 24 of 125 samples (19.2%) by RT-qPCR and isolated from 14 (11.2%) in cell cultures. It was detected in 80.9% (17/21) and cultured from 61.9% (13/21) of air samples collected using water condensation samplers, compared to swab samples which had a RT-qPCR detection rate of 10.5% (4/38) and virus isolation rate of 2.63% (1/38). No statistically significant differences existed in the likelihood of virus detection by RT-qPCR or amount of infectious virus in the air between areas of primary and secondary occupancy within residences. Our work provides information about the presence of SARS-CoV-2 in the air within homes of individuals with COVID-19. Information herein can help individuals make informed decisions about personal exposure risks when sharing indoor spaces with infected individuals isolating at home and further inform health departments and the public about SARS-CoV-2 exposure risks within residences.

Toxicity assessment of CeO2 and CuO nanoparticles at the air-liquid interface using bioinspired condensational particle growth.

CeO2 and CuO nanoparticles (NPs) are used as additives in petrodiesel to enhance engine performance leading to reduced diesel combustion emissions. Despite their benefits, the additive application poses human health concerns by releasing inhalable NPs into the ambient air. In this study, a bioinspired lung cell exposure system, Dosimetric Aerosol in Vitro Inhalation Device (DAVID), was employed for evaluating the toxicity of aerosolized CeO2 and CuO NPs with a short duration of exposure (≤10 min vs. hours in other systems) and without exerting toxicity from non-NP factors. Human epithelial A549 lung cells were cultured and maintained within DAVID at the air-liquid interface (ALI), onto which aerosolized NPs were deposited, and experiments in submerged cells were used for comparison. Exposure of the cells to the CeO2 NPs did not result in detectable IL-8 release, nor did it produce a significant reduction in cell viability based on lactate dehydrogenase (LDH) assay, with a marginal decrease (10%) at the dose of 388 µg/cm2 (273 cm2/cm2). In contrast, exposure to CuO NPs resulted in a concentration dependent reduction in LDH release based on LDH leakage, with 38% reduction in viability at the highest dose of 52 µg/cm2 (28.3 cm2/cm2). Cells exposed to CuO NPs resulted in a dose dependent cellular membrane toxicity and expressed IL-8 secretion at a global dose five times lower than cells exposed under submerged conditions. However, when comparing the ALI results at the local cellular dose of CuO NPs to the submerged results, the IL-8 secretion was similar. In this study, we demonstrated DAVID as a new exposure tool that helps evaluate aerosol toxicity in simulated lung environment. Our results also highlight the necessity in choosing the right assay endpoints for the given exposure scenario, e.g., LDH for ALI and Deep Blue for submerged conditions for cell viability.

Preparation of Gd-doped AuNBP@mSiO2 nanocomposites for the MR imaging, drug delivery and chemo-photothermal synergistic killing of breast cancer cells.

Under near-infrared (NIR) light, gold nanobipyramids (AuNBPs) exhibit a high photothermal conversion rate and photothermal stability, making them ideal mediators for photothermal therapy (PTT). In this study, highly purified AuNBPs are prepared, followed by coating their surfaces with mesoporous silica (mSiO2). The obtained AuNBP@mSiO2 nanocomplex exhibits an ellipsoidal shape with a relatively large specific surface, pore diameter and pore volume. To achieve MRI guided chemo-photothermal therapy of breast cancer cells, the nanocomplex is further coupled with the MRI contrast agent Gd-DTTA and the chemotherapeutic drug doxorubicin (DOX). The results indicated that under NIR light irradiation, AuNBPs exhibited promising PTT effects, while the cumulative release rate of DOX was significantly enhanced to 81.40%. Moreover, the chemo-photothermal therapy approach effectively eradicated 4T1 breast cancer cells. This work successfully confirms that chemo-photothermal synergistic therapy is an effective tumor treatment strategy and demonstrates the potential application of AuNBP@mSiO2 as a nano-drug delivery platform. Additionally, it introduces new ideas for the integrated study of breast cancer diagnosis and treatment.

Assessment of Scanning Mobility Particle Sizer (SMPS) for online monitoring of delivered dose in an in vitro aerosol exposure system.

Real-time monitoring of dosimetry is critical to mitigating the constraints of offline measurements. To address this need, the use of the Scanning Mobility Particle Sizer (SMPS) to estimate the dose delivered through the Dosimetric Aerosol in Vitro Inhalation Device (DAVID) was assessed. CuO nanoparticles suspended in ethanol at different concentrations (0.01-10 mg/mL) were aerosolized using a Collison nebulizer and diluted with air at a ratio of either 1:3 (setup 1) or 1:18 (setup 2). From the aerosol volume concentrations measured by the SMPS, density of CuO (6.4 g/cm3), collection time (5-30 min), flow rate (0.5 LPM) and deposition area (0.28 cm2), the mass doses (DoseSMPS) were observed to increase exponentially over time and ranged from 0.02 ± 0.001 to 84.75 ± 3.49 µg/cm2. The doses calculated from the Cu concentrations determined by Inductively Coupled Plasma-Optical Emission Spectrometry (ICP-OES) (DoseICP) also increased exponentially over time (0.01 ± 0.01-97.25 ± 1.30 µg/cm2). Regression analysis between DoseICP and DoseSMPS showed R2 ≥ 0.90 for 0.1-10 mg/mL. As demonstrated, the SMPS can be used to monitor the delivered dose in real-time, and controlled delivery of mass doses with a 226-fold range can be attained in ≤30 min in DAVID by adjusting the nebulizer concentration, dilution air and time.

Lack of SARS-CoV-2 in environmental samples collected from September 2020-February 2021 in a university that followed CDC reopening guidance.

This study aimed to provide environmental surveillance data for evaluating the risk of acquiring SARS-CoV-2 in public areas with high foot traffic in a university. Air and surface samples were collected at a university that had the second highest number of COVID-19 cases among public higher education institutions in the U.S. during Fall 2020. A total of 60 samples were collected in 16 sampling events performed during Fall 2020 and Spring 2021. Nearly 9800 students traversed the sites during the study period. SARS-CoV-2 was not detected in any air or surface samples. The university followed CDC guidance, including COVID-19 testing, case investigations, and contact tracing. Students, faculty, and staff were asked to maintain physical distancing and wear face coverings. Although COVID-19 cases were relatively high at the university, the possibility of acquiring SARS-CoV-2 infections at the sites tested was low.

Efficient adsorption of aromatic and aliphatic hydrocarbons by electrospun hydrophobic PTFE-NiO composite nanofiber filter mats.

Aromatic and aliphatic hydrocarbons (AAHs) are comprised of a variety of gaseous chemicals that may affect human and environmental health. To remove AAHs from air, polytetrafluoroethylene-nickel oxide (PTFE-NiO) composite nanofiber filter mats (NFMs) were synthesized and characterized for their ability to effectively adsorb AAHs. The NiO-nanoparticle-doped mats were fabricated by green electrospinning of PTFE and polyvinyl alcohol (PVA) mixtures added with nickel (II) nitrate hexahydrate in the spinning solution followed by surface heat treatment. FE-SEM FTIR, Raman spectroscopy, sessile drop and Jar methods were applied as characterization techniques. The diameter of the electrospun nanofibers without NiO dopant ranged from 0.34 ± 21.61 to 0.23 ± 10.12 µm, whereas a reduction in diameter of NiO-doped nanofibers was obtained, ranging between pristine to 0.25 ± 24.12 µm and 0.12 ± 85.75 µm with heat treatment. 6% (by weight) NiO-doped PTFE composite NFMs exhibited a high water-contact angle of 120 ± 2.20 degrees; the high hydrophobicity value aided self-cleansing property of NFMs for practical applications. UV adsorption capability for heat-treated PTFE-NiO NFMs was evaluated for three AAHs, and the results showed that 6 wt% NiO adsorbed 1.41, 0.67, and 0.73 µg/mg of toluene, formaldehyde and acetone, respectively. These findings reveal the potential applicability of the prepared filter mats for capturing various AAHs from polluted air.

A Remote Monitoring System for Rodent Infestation Based on LoRaWAN.

Rodent infestations are a common problem that can result in several issues, including diseases, damage to property, and crop loss. Conventional methods of controlling rodent infestations often involve using mousetraps and applying rodenticides manually, leading to high manpower expenses and environmental pollution. To address this issue, we introduce a system for remotely monitoring rodent infestations using Internet of Things (IoT) nodes equipped with Long Range (LoRa) modules. The sensing nodes wirelessly transmit data related to rodent activity to a cloud server, enabling the server to provide real-time information. Additionally, this approach involves using images to auxiliary detect rodent activity in various buildings. By capturing images of rodents and analyzing their behavior, we can gain insight into their movement patterns and activity levels. By visualizing the recorded information from multiple nodes, rodent control personnel can analyze and address infestations more efficiently. Through the digital and quantitative sensing technology proposed at this stage, it can serve as a new objective indicator before and after the implementation of medication or other prevention and control methods. The hardware cost for the proposed system is approximately USD 43 for one sensor module and USD 17 for one data collection gateway (DCG). We also evaluated the power consumption of the sensor module and found that the 3.7 V 18,650 Li-ion batteries in series can provide a battery life of two weeks. The proposed system can be combined with rodent control strategies and applied in real-world scenarios such as restaurants and factories to evaluate its performance.

A cell wall-targeted organic-inorganic hybrid nano-catcher for ultrafast capture and SERS detection of invasive fungi.

Due to the extended culture period and various inconveniences in vitro culture, the detection of invasive fungi is rather difficult, leading to high mortality rates of the diseases caused by them. It is, however, crucial for clinical therapy and lowering patient mortality to quickly identify invasive fungus from clinical specimens. A promising non-destructive method for finding fungi is surface-enhanced Raman scattering (SERS), however, its substrate has a low level of selectivity. Clinical sample components can obstruct the target fungi's SERS signal on account of their complexity. Herein, an MNP@PNIPAMAA hybrid organic-inorganic nano-catcher was created by using ultrasonic-initiated polymerization. The caspofungin (CAS), a fungus cell wall-targeting drug, is used in this study. We investigated MNP@PNIPAMAA-CAS as a technique to rapidly extract fungus from complex samples under 3 s. SERS could subsequently be used to instantly identify the fungi that were successfully isolated with an efficacy rate of about 75%. The entire process took just 10 min. This method is an important breakthrough that might be advantageous in terms of the rapid detection of invasive fungi.

Identification of geographic origins of Morus alba Linn. through surfaced enhanced Raman spectrometry and machine learning algorithms.

The leaves of Morus alba Linn., which is also known as white mulberry, have been commonly used in many of traditional systems of medicine for centuries. In traditional Chinese medicine (TCM), mulberry leaf is mainly used for anti-diabetic purpose due to its enrichment in bioactive compounds such as alkaloids, flavonoids and polysaccharides. However, these components are variable due to the different habitats of the mulberry plant. Therefore, geographic origin is an important feature because it is closely associated with bioactive ingredient composition that further influences medicinal qualities and effects. As a low-cost and non-invasive method, surface enhanced Raman spectrometry (SERS) is able to generate the overall fingerprints of chemical compounds in medicinal plants, which holds the potential for the rapid identification of their geographic origins. In this study, we collected mulberry leaves from five representative provinces in China, namely, Anhui, Guangdong, Hebei, Henan and Jiangsu. SERS spectrometry was applied to characterize the fingerprints of both ethanol and water extracts of mulberry leaves, respectively. Through the combination of SERS spectra and machine learning algorithms, mulberry leaves were well discriminated with high accuracies in terms of their geographic origins, among which the deep learning algorithm convolutional neural network (CNN) showed the best performance. Taken together, our study established a novel method for predicting the geographic origins of mulberry leaves through the combination of SERS spectra with machine learning algorithms, which strengthened the application potential of the method in the quality evaluation, control and assurance of mulberry leaves.

Marine Fish Protein Peptide Regulating Potassium Oxonate-Induced Intestinal Dysfunction in Hyperuricemia Rats Helps Alleviate Kidney Inflammation.

The metabolic disease hyperuricemia (HUA) is characterized by a disturbance in purine metabolism. Peptides, such as marine fish-derived peptides, have previously been shown to be effective in alleviating HUA. In this study, HUA rats were induced by potassium oxonate with 100 mg/kg (L), 200 mg/kg (M), and 400 mg/kg (H) of marine fish protein peptide (MFPP). The results showed that MFPP could effectively reduce the serum uric acid (SUA) levels compared with the model group rats; kidney histopathology and the levels of inflammatory factors (TNF-α, IL-6, and IL-10) indicated that MFPP attenuated HUA-induced kidney inflammation. Meanwhile, MFPP restored the abundance of beneficial bacteria, including Lactobacillus, Blautia, Colidextribacter, and Intestinimonas. MFPP further repaired the intestinal barrier by recovering the expression of gene Ildr2 encoding the tricellular tight junction protein ILDR2 and the immune-related genes Ccr7 and Nr4a3 and also regulated the expression of Entpd8 and Cyp27b1 to restore kidney function and uric acid metabolism. MFPP was proved to have potential as a therapeutic strategy to be included in dietary intervention to relieve HUA.

Fatal rectovaginal fistula in post-radiotherapy locally advanced cervical cancer patients.

OBJECTIVE: To present the detailed history of three cervical cancer patients with rectovaginal fistula, who had undergone radiotherapy. CASES REPORT: A 74-year-old patient with end-stage renal disease undergoing hemodialysis had radiotherapy for her advanced cervical cancer. Colonoscopic biopsy showed radiation sigmoid colitis and ulcers. Laparotomy revealed colon perforation and rectovaginal fistula. The second case is a 54-year-old cervical cancer patient, who had received concurrent chemoradiation therapy and further systemic therapy with cisplatin, paclitaxel, and bevacizumab. She suffered from bloody stool and abdominal pain. Rectovaginal fistula was found during exploratory laparotomy. The third case is a 35-year-old cervical cancer patient, who had received concurrent chemoradiation therapy. Systemic therapy was then prescribed with platinum, paclitaxel, and bevacizumab for her lung metastasis, and a rectovaginal fistula was found later. All three patients did not survive later. CONCLUSIONS: Fatal rectovaginal fistula may occur in post-radiation advanced cervical cancer patients. Unnecessary colonoscopic biopsy may cause significant sequelae. In patients with high risk for rectovaginal fistulas, chemotherapy without adding bevacizumab might be suggested in patients with low risk of poor response to chemotherapy. In addition, three-dimensional conformal radiation therapy or intensity-modulated radiation therapy should be used for patients with high risk for fistulas.

Machine learning analysis of SERS fingerprinting for the rapid determination of Mycobacterium tuberculosis infection and drug resistance.

Over the past decades, conventional methods and molecular assays have been developed for the detection of tuberculosis (TB). However, these techniques suffer limitations in the identification of Mycobacterium tuberculosis (Mtb), such as long turnaround time and low detection sensitivity, etc., not even mentioning the difficulty in discriminating antibiotics-resistant Mtb strains that cause great challenges in TB treatment and prevention. Thus, techniques with easy implementation for rapid diagnosis of Mtb infection are in high demand for routine TB diagnosis. Due to the label-free, low-cost and non-invasive features, surface enhanced Raman spectroscopy (SERS) has been extensively investigated for its potential in bacterial pathogen identification. However, at current stage, few studies have recruited handheld Raman spectrometer to discriminate sputum samples with or without Mtb, separate pulmonary Mtb strains from extra-pulmonary Mtb strains, or profile Mtb strains with different antibiotic resistance characteristics. In this study, we recruited a set of supervised machine learning algorithms to dissect different SERS spectra generated via a handheld Raman spectrometer with a focus on deep learning algorithms, through which sputum samples with or without Mtb strains were successfully differentiated (5-fold cross-validation accuracy = 94.32%). Meanwhile, Mtb strains isolated from pulmonary and extra-pulmonary samples were effectively separated (5-fold cross-validation accuracy = 99.86%). Moreover, Mtb strains with different drug-resistant profiles were also competently distinguished (5-fold cross-validation accuracy = 99.59%). Taken together, we concluded that, with the assistance of deep learning algorithms, handheld Raman spectrometer has a high application potential for rapid point-of-care diagnosis of Mtb infections in future.

Identification of Bacterial Pathogens at Genus and Species Levels through Combination of Raman Spectrometry and Deep-Learning Algorithms.

The rapid and accurate identification of the causing agents during bacterial infections would greatly improve pathogen transmission, prevention, patient care, and medical treatments in clinical settings. Although many conventional and molecular methods have been proven to be efficient and reliable, some of them suffer technical biases and limitations that require the development and application of novel and advanced techniques. Recently, due to its cost affordability, noninvasiveness, and label-free feature, Raman spectroscopy (RS) is emerging as a potential technique for fast bacterial detection. However, the method is still hampered by many technical issues, such as low signal intensity, poor reproducibility, and standard data set insufficiency, among others. Thus, it should be cautiously claimed that Raman spectroscopy could provide practical applications in real-world settings. In order to evaluate the implementation potentials of Raman spectroscopy in the identification of bacterial pathogens, we investigated 30 bacterial species belonging to 9 different bacterial genera that were isolated from clinical samples via surfaced enhanced Raman spectroscopy (SERS). A total of 17,149 SERS spectra were harvested from a Raman spectrometer and were further analyzed via machine learning approaches, which showed that a convolutional neural network (CNN) deep learning algorithm achieved the highest prediction accuracy for recognizing pathogenic bacteria at both the genus and species levels. In summary, the SERS technique holds a promising potential for fast bacterial pathogen identification in clinical laboratories with the integration of machine learning algorithms, which might be further developed and sharpened for the direct identification and prediction of bacterial pathogens from clinical samples. IMPORTANCE In this study, we investigated 30 bacterial species belonging to 9 different bacterial genera that were isolated from clinical samples via surfaced enhanced Raman spectroscopy (SERS). A total of 17,149 SERS spectra were harvested from a Raman spectrometer and were further analyzed via machine learning approaches, the results of which showed that the convolutional neural network (CNN) deep learning algorithm could achieve the highest prediction accuracy for recognizing pathogenic bacteria at both the genus and species levels. Taken together, we concluded that the SERS technique held a promising potential for fast bacterial pathogen diagnosis in clinical laboratories with the integration of deep learning algorithms, which might be further developed and sharpened for the direct identification and prediction of bacterial pathogens from clinical samples.

A SERS Platform for Rapid Detection of Drug Resistance of Non-Candida albicans Using Fe3O4@PEI and Triangular Silver Nanoplates.

Purpose: Candida infection has a high mortality rate, and the increasing prevalence of non-Candida albicans drug resistance in recent years poses a potential threat to human health. Non-Candida albicans has long culture cycles, and its firm cell walls making it difficult to isolate DNA for sequencing. Materials and Methods: Fe3O4@PEI (PEI, polyvinyl imine) was mixed with clinical samples to form Fe3O4@PEI@non-Candida albicans and enriched them with magnets. Triangular silver nanoplates enhanced the surface-enhanced Raman scattering (SERS) signal. SERS was used to detect the fingerprint spectrum of non-Candida albicans. Then, orthogonal partial least squares discriminant analysis (OPLS-DA) was used to analyze the drug resistance of non-Candida albicans. Results: SERS combined with OPLS-DA could well analyze the drug resistance of non-Candida albicans. Through 10-fold-cross validation, the accuracy of training and test data is greater than 99%, indicating that the model has good classification ability. We used SERS for the first time to detect the drug resistance of non-Candida albicans directly. Conclusion: This approach can be utilized without causing damage to the cell wall and can be accomplished in as little as 90 minutes. It can provide timely guidance for the treatment of patients with good clinical application potential.

Identification of Thresholds on Population Density for Understanding Transmission of COVID-19.

Pathways of transmission of coronavirus (COVID-19) disease in the human population are still emerging. However, empirical observations suggest that dense human settlements are the most adversely impacted, corroborating a broad consensus that human-to-human transmission is a key mechanism for the rapid spread of this disease. Here, using logistic regression techniques, estimates of threshold levels of population density were computed corresponding to the incidence (case counts) in the human population. Regions with population densities greater than 3,000 person per square mile in the United States have about 95% likelihood to report 43,380 number of average cumulative cases of COVID-19. Since case numbers of COVID-19 dynamically changed each day until 30 November 2020, ca. 4% of US counties were at 50% or higher probability to 38,232 number of COVID-19 cases. While threshold on population density is not the sole indicator for predictability of coronavirus in human population, yet it is one of the key variables on understanding and rethinking human settlement in urban landscapes.