BACKGROUND: The online pharmacy market is growing, with legitimate online pharmacies offering advantages such as convenience and accessibility. However, this increased demand has attracted malicious actors into this space, leading to the proliferation of illegal vendors that use deceptive techniques to rank higher in search results and pose serious public health risks by dispensing substandard or falsified medicines. Search engine providers have started integrating generative artificial intelligence (AI) into search engine interfaces, which could revolutionize search by delivering more personalized results through a user-friendly experience. However, improper integration of these new technologies carries potential risks and could further exacerbate the risks posed by illicit online pharmacies by inadvertently directing users to illegal vendors.
OBJECTIVE: The role of generative AI integration in reshaping search engine results, particularly related to online pharmacies, has not yet been studied. Our objective was to identify, determine the prevalence of, and characterize illegal online pharmacy recommendations within the AI-generated search results and recommendations.
METHODS: We conducted a comparative assessment of AI-generated recommendations from Google\'s Search Generative Experience (SGE) and Microsoft Bing\'s Chat, focusing on popular and well-known medicines representing multiple therapeutic categories including controlled substances. Websites were individually examined to determine legitimacy, and known illegal vendors were identified by cross-referencing with the National Association of Boards of Pharmacy and LegitScript databases.
RESULTS: Of the 262 websites recommended in the AI-generated search results, 47.33% (124/262) belonged to active online pharmacies, with 31.29% (82/262) leading to legitimate ones. However, 19.04% (24/126) of Bing Chat\'s and 13.23% (18/136) of Google SGE\'s recommendations directed users to illegal vendors, including for controlled substances. The proportion of illegal pharmacies varied by drug and search engine. A significant difference was observed in the distribution of illegal websites between search engines. The prevalence of links leading to illegal online pharmacies selling prescription medications was significantly higher (P=.001) in Bing Chat (21/86, 24%) compared to Google SGE (6/92, 6%). Regarding the suggestions for controlled substances, suggestions generated by Google led to a significantly higher number of rogue sellers (12/44, 27%; P=.02) compared to Bing (3/40, 7%).
CONCLUSIONS: While the integration of generative AI into search engines offers promising potential, it also poses significant risks. This is the first study to shed light on the vulnerabilities within these platforms while highlighting the potential public health implications associated with their inadvertent promotion of illegal pharmacies. We found a concerning proportion of AI-generated recommendations that led to illegal online pharmacies, which could not only potentially increase their traffic but also further exacerbate existing public health risks. Rigorous oversight and proper safeguards are urgently needed in generative search to mitigate consumer risks, making sure to actively guide users to verified pharmacies and prioritize legitimate sources while excluding illegal vendors from recommendations.

Genetic improvement mainly depends on the level of genetic variability present in the population, and the degree of genetic diversity in a population largely determines the rate of genetic advancement. For analyzing genetic diversity and determining cultivar identities, a molecular marker is a useful tool. Using 30 SSR (simple sequence repeat) and 30 RAPD (randomly amplified polymorphic DNA) markers, this study evaluated the genetic divergence of 17 mango cultivars. The effectiveness of the two marker systems was evaluated using their genetic diversity characteristics. Additionally, the effects of SM (simple matching) and Dice similarity coefficients and their effects on mango clustering were evaluated. The findings showed that SSR markers generated 192 alleles, all of which were polymorphic (100%). With RAPD markers, 434 bands were obtained, 361 of which were polymorphic (83%). The average polymorphic information content (PIC) for RAPD and SSR was 0.378 and 0.735, respectively. Using SSR markers resulted in much higher values for other genetic diversity parameters compared to RAPD markers. Furthermore, grouping the genotypes according to the two similarity coefficients without detailed consideration of these coefficients could not influence the study results. The RAPD markers OPA_01, OPM_12 followed by OPO_12 and SSR markers MIAC_4, MIAC_5 followed by mMiCIR_21 were the most informative in terms of describing genetic variability among the cultivars under study; they can be used in further investigations such as genetic mapping or marker-assisted selection. Overall, \'Zebda\' cultivar was the most diverse of the studied cultivars.

In this study, utilization of a bio-based fuel in a turbojet engine is comprehensively monitored with adapting various useful indicators for the scope of the study based on thermodynamic principles. In this regard, extensive energy and exergy, thermoecologic, environmental, enviroeconomic and sustainability analyses are performed for both the turbojet engines fueled by jet kerosene and fueled by a bio-based fuel. As per the main findings, the mass stream of combustion emissions is measured to be 4.547 kg s-1, when the engine is powered by biofuel. The specific fuel consumption and specific thrust are determined as 0.13 kg kN-1 s-1 and 147.81 kNs kg-1 for jet kerosene-powered case, while they are calculated as 0.15 kg kN-1 s-1 and 148.23 kNs kg-1 for biofuel-powered case. If biofuel is selected over jet-kerosene fuel, it is observed that the engine has better energy efficiency performance by 18.18%. The engine\'s environmental effect factor value is found as 4.88 for jet-kerosene usage condition, while it is found to be 4.93 for biofuel utilization case. The overall emitted CO2 emissions is measured as 336,672 kg-CO2 year-1 for jet-kerosene usage condition, while it is estimated as 222,012 kg-CO2 year-1 for the biofuel utilization case. Also, as far as biofuel is chosen as alternative to jet-kerosene, the environmental damage cost stream, namely the enviroeconomic parameter of the engine, falls from 59,254.27 US$ year-1 to 39,074.11 US$ year-1. It is observed that sustainable efficiency factor and exergetic sustainability index outputs of the components of air compressor are the same for jet-kerosene and biofuel utilization cases, which are 8.31 and 7.31, respectively.

This paper makes the first comparative assessment of the impacts of the first and second waves of the ongoing COVID-19 pandemic for the US stock market and its uncertainty. To this end, we investigate the dynamic conditional correlation and the asymmetric impacts of shocks on the correlation between the US and Chinese stock markets before and during the COVID-19 crisis. Furthermore, we analyze and compare the relationship between the COVID-19 pandemic and US returns and uncertainty during the first and second waves of the pandemic. First, we find that the dynamic correlation approach supports the presence of volatility spillovers (contagion effects) between the two stock markets, especially during the rapid spread phase of COVID-19 in the US. Second, the analysis of news impact correlation surfaces shows that the shocks to the US and Chinese markets have asymmetric effects on the correlation between the two markets. Finally, we find a persistent link between US returns, uncertainty, and the COVID-19 pandemic during the first and second waves of the outbreak. Our results prove that the pandemic has shown harmful consequences for financial markets in general and the US economy in particular.

UNASSIGNED: The accurate definition of gross tumor volume (GTV) of esophageal squamous cell carcinoma (ESCC) can promote precise irradiation field determination, and further achieve the radiotherapy curative effect. This retrospective study is intended to assess the applicability of leveraging deep learning-based method to automatically define the GTV from 3D 18F-FDG PET/CT images of patients diagnosed with ESCC.
UNASSIGNED: We perform experiments on a clinical cohort with 164 18F-FDG PET/CT scans. The state-of-the-art esophageal GTV segmentation deep neural net is first employed to delineate the lesion area on PET/CT images. Afterwards, we propose a novel equivalent truncated elliptical cone integral method (ETECIM) to estimate the GTV value. Indexes of Dice similarity coefficient (DSC), Hausdorff distance (HD), and mean surface distance (MSD) are used to evaluate the segmentation performance. Conformity index (CI), degree of inclusion (DI), and motion vector (MV) are used to assess the differences between predicted and ground truth tumors. Statistical differences in the GTV, DI, and position are also determined.
UNASSIGNED: We perform 4-fold cross-validation for evaluation, reporting the values of DSC, HD, and MSD as 0.72 ± 0.02, 11.87 ± 4.20 mm, and 2.43 ± 0.60 mm (mean ± standard deviation), respectively. Pearson correlations (R2) achieve 0.8434, 0.8004, 0.9239, and 0.7119 for each fold cross-validation, and there is no significant difference (t = 1.193, p = 0.235) between the predicted and ground truth GTVs. For DI, a significant difference is found (t = -2.263, p = 0.009). For position assessment, there is no significant difference (left-right in x direction: t = 0.102, p = 0.919, anterior-posterior in y direction: t = 0.221, p = 0.826, and cranial-caudal in z direction: t = 0.569, p = 0.570) between the predicted and ground truth GTVs. The median of CI is 0.63, and the gotten MV is small.
UNASSIGNED: The predicted tumors correspond well with the manual ground truth. The proposed GTV estimation approach ETECIM is more precise than the most commonly used voxel volume summation method. The ground truth GTVs can be solved out due to the good linear correlation with the predicted results. Deep learning-based method shows its promising in GTV definition and clinical radiotherapy application.

UNASSIGNED: Despite increasing interest in whole-slide imaging (WSI) over optical microscopy (OM), limited information on comparative assessment of various digital pathology systems (DPSs) is available.
UNASSIGNED: A comprehensive evaluation was undertaken to investigate the technical performance-assessment and diagnostic accuracy of four DPSs with an objective to establish the noninferiority of WSI over OM and find out the best possible DPS for clinical workflow.
UNASSIGNED: A total of 2376 digital images, 15,775 image reads (OM - 3171 + WSI - 12,404), and 6100 diagnostic reads (OM - 1245, WSI - 4855) were generated across four DPSs (coded as DPS: 1, 2, 3, and 4) using a total 240 cases (604 slides). Onsite technical evaluation revealed successful scan rate: DPS3 < DPS2 < DPS4 < DPS1; mean scanning time: DPS4 < DPS1 < DPS2 < DPS3; and average storage space: DPS3 < DPS2 < DPS1 < DPS4. Overall diagnostic accuracy, when compared with the reference standard for OM and WSI, was 95.44% (including 2.48% minor and 2.08% major discordances) and 93.32% (including 4.28% minor and 2.4% major discordances), respectively. The difference between the clinically significant discordances by WSI versus OM was 0.32%. Major discordances were observed mostly using DPS4 and least in DPS1; however, the difference was statistically insignificant. Almost perfect (κ ≥ 0.8)/substantial (κ = 0.6-0.8) inter/intra-observer agreement between WSI and OM was observed for all specimen types, except cytology. Overall image quality was best for DPS1 followed by DPS4. Mean digital artifact rate was 6.8% (163/2376 digital images) and maximum artifacts were noted in DPS2 (n = 77) followed by DPS3 (n = 36). Most pathologists preferred viewing software of DPS1 and DPS2.
UNASSIGNED: WSI was noninferior to OM for all specimen types, except for cytology. Each DPS has its own pros and cons; however, DPS1 closely emulated the real-world clinical environment. This evaluation is intended to provide a roadmap to pathologists for the selection of the appropriate DPSs while adopting WSI.

China encounters heavy air pollution caused by coal consumption. China and the EU aim to decrease greenhouse gas emissions. Shifting to biogas from residues contributes to solving both problems. This study assesses China\'s biogas potentials and related water footprints (WFs) and compares results with potentials and WFs for the EU. Starting from a literature review on EU biogas potentials, it analyzes information resulting in a calculation methodology, its validation and application to China. Finally, it estimates WFs and makes a comparative assessment of biogas potentials of the EU and China. In the EU, biogas from agricultural, forestry and other residues might contribute 8% (5300 PJ) to primary energy consumption, in China 10% (13,275 PJ.) In the EU, agriculture contributes 41%, forestry 26%, other residues 23%, and manure 10%. The corresponding results for China are agriculture (67%), forestry (23%), manure (7%) and other residues (3%). In the EU, biogas might contribute 45% to total gas demand; in China more biogas can be produced than consumed in 2018 (185% of demand). The EU results fall in the range of residue potentials from earlier studies. Maize, wheat, barley and rapeseed contribute 78% to the EU agricultural biogas potential. In China, dominant crops are maize (49%), rice (18%), wheat (12%) and seed cotton (6%). For water, there are large differences among WFs of specific crop residues, but also between WFs for EU and Chinese crop residues. Most Chinese crop residues have larger WFs than the EU residues. Biogas from sugar beet residues has the smallest WFs, biogas from tobacco residues the largest. Although using residues for energy does not change total national WFs, it reallocates WFs over main products and residues. The comparative assessment supports better use of biogas potentials from residues with lower WFs and is also applicable for other regions and countries.

Thermally driven heat pump systems play important roles in the utilization of low-grade thermal energy. In order to evaluate and compare the performances of three different constructions of thermally driven heat pump and heat transformer, the low-dissipation assumption has been adopted to establish the irreversible thermodynamic models of them in the present paper. By means of the proposed models, the heating loads, the coefficients of performance (COPs) and the optimal relations between them for various constructions are derived and discussed. The performances of different constructions are numerically assessed. More importantly, according to the results obtained, the upper and lower bounds of the COP at maximum heating load for different constructions are generated and compared by the introduction of a parameter measuring the deviation from the reversible limit of the system. Accordingly, the optimal constructions for the low-dissipation three-terminal heat pump and heat transformer are determined within the frame of low-dissipation assumption, respectively. The optimal constructions in accord with previous research and engineering practices for various three-terminal devices are obtained, which confirms the compatibility between the low-dissipation model and endoreversible model and highlights the validity of the application of low-dissipation model for multi-terminal thermodynamic devices. The proposed models and the significant results obtained enrich the theoretical thermodynamic model of thermally driven heat pump systems and may provide some useful guidelines for the design and operation of realistic thermally driven heat pump systems.

There are two main categories of dryness monitoring indices based on spectral feature space. One category uses the vertical distance from any point to a line passing through the coordinate origin, which is perpendicular to a soil line, to monitor the dryness conditions. The most popular indices are the Perpendicular Dryness Index (PDI) and the modified perpendicular dryness index (MPDI). The other category uses the distance from any point in feature space to the coordinate origin to represent the dryness status, for instance, the soil moisture (SM) monitoring index (SMMI) and the modified soil moisture monitoring index (MSMMI). In this study, the performances and differences of these four indicators were evaluated using field-measured SM (FSM) data based on Gaofen-1 (GF-1) wide field of view (WFV), Landsat-8 Operational Land Imager (OLI), and Sentinel-2 Multi-Spectral Instrument (MSI) sensors. Performance evaluations were conducted in two study areas, namely an arid and semi-arid region of northwest China and a humid agricultural region of southwest Canada. We employed gradient-based structural similarity (GSSIM) to quantitatively assess the similarity of the structural information and structural characteristics among these four indicators. Monitoring SM in bare soil or low vegetation-covered areas in the semi-arid region, the SMMI, PDI, MSMMI, and MPDI from Near-Infrared (NIR)-Red had significantly negative linear correlations with the FSM at 0-5-cm depth (P < 0.01). However, SMMI was better than PDI in estimating SM in bare soil, which was better than MSMMI and MPDI for GF-1. Moreover, the PDI and SMMI had similar SM evaluation abilities, which were better than those of MPDI and MSMMI for Landsat-8. The GSSIM map of the SMMI/PDI and the MSMMI/MPDI showed that the low change areas accounted for 99.89% and 98.89% for GF-1, respectively, and 95.78% and 94.45% for Landsat-8, respectively. This result indicated that the SMMI, PDI, MSMMI, and MPDI values from NIR-Red in low vegetation cover were similar. In monitoring SM in agricultural vegetation areas, the accuracy of the four indices from Short-Wave Infrared (SWIR) feature space was higher than that from NIR-Red feature space for Sentinel-2. The SM monitoring effect of MSMMI and MPDI was better than that of SMMI and PDI. Due to the lack of SWIR band, GF-1 was limited in monitoring SM in vegetation-covered areas. The SMMI and MSMMI, which do not rely on the soil line, were more suitable than PDI and MPDI for retrieving SM in the complex surface environment depending on the soil line and the number of parameters. GF-1 with 16-m resolution had higher accuracy in SM assessment than Landsat-8 with 30-m resolution and had almost the same accuracy as Sentinel-2 with 20 m.

The removal of arsenic from groundwater is an important issue for environmental safety and human health. Research focused on the comparative assessment of arsenic removal from arsenic-bearing groundwater and arsenic-containing-synthetic water (2 mg/L) using natural magnetic material (NMM) (rock) and synthesized magnetic material (SMM) by Bacillus pasteurii and humic acid. The arsenic-bearing groundwater (97.56 ± 0.05 μg/L) exceed the WHO limit (10 μg/L) of arsenic concentration for drinking water. The NMM contains dominantly magnetite, hematite, ferrihydrate, coesite, quartz, and stishovite. The NMM of natural rock exhibited the existence of iron (6.25-8.86% Fe3O4), which is widespread and important component in sedimentary rocks. The investigation on vibrating sample magnetometers (VSM) of NMM and SMM demonstrated the typical magnetization properties, which can be separated after arsenic removal process. The thermogravimetric analysis (TGA) of SMM displayed the existence of organic matter decomposition during particle synthesis. The TEM and SEM exhibited the nanoparticle particle formation within the range of 10-39 nm (10-20 nm particle Fe3O4 through B. pasteurii). FTIR spectrum (before and after removal of arsenic) indicated the existence and binding nature in between arsenic and iron. >90% of arsenic was removed from arsenic-bearing groundwater using Fe3O4, Fe3O4 (N2-Environment), Fe3O4 with humic acid, and Fe3O4 with B. pasteurii after 25 min, 8 min, 13 min and 120 min, respectively. In case of NMM in Site-A, the arsenic removal was observed very fast as 85-87% within 30 s, whereas 95-99%, 93-95% and 88-91% removal detected using the sample of Site-A, Site-B, and Site-C respectively, after 120 min at natural pH (8.31 ± 0.05) of arsenic-bearing groundwater. Thus, NMM, (ecofriendly green material), can be applicable for arsenic removal from arsenic-bearing groundwater.