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Mycobacterium kansasii (M. kansasii) pulmonary disease is frequently misdiagnosed and treated as tuberculosis, especially in countries with high tuberculosis burden. This study aimed to investigate the drug resistance profile of M. kansasii in patients with M. kansasii pulmonary disease in Shanghai, and to determine the variations in drug resistance after the 2-month antimycobacterial treatment.All patients diagnosed with M. kansasii pulmonary disease from 2017 to 2019 in Shanghai were retrospectively analyzed. Whole-genome sequencing (WGS) was performed and minimum inhibitory concentration (MIC) to antimycobacterial drugs was measured by broth microdilution method.In total, 191 patients were diagnosed with M. kansasii pulmonary disease. Of them, 24.1% (46/191) had persistent positive culture after two months antimycobacterial treatment. WGS found that the 46 paired isolates had a difference17 SNPs, thus excluding the possibility of exogenous reinfection. Over 90% of the baseline isolates were sensitive to either of rifampin, clarithromycin, moxifloxacin, and amikacin, while a high resistance to ethambutol (118/191, 61.8%) and 4 μg/ml isoniazid (32/191, 16.8%) were observed. Two isolates presented high resistance to rifamycin, i.e., rifampin MIC8 μg/mL and rifabutin MIC = 8 μg/mL, both containing the rpoB mutation (S454L). The increase of MIC to rifampin, ethambutol, and/or isoniazid was identified in 56.5% (26/46) of patients.A high prevalence of innate resistance to ethambutol and isoniazid was observed among circulating M. kansasii clinical strains in Shanghai. The increase of drug resistance under empirical antimycobacterial treatment highlighted the urgency of definitive species identification before initiating treatment.

Motivation Human gut microbiota plays a vital role in maintaining body health. The dysbiosis of gut microbiota is associated with a variety of diseases. It is critical to uncover the associations between gut microbiota and disease states as well as other intrinsic or environmental factors. However, inferring alterations of individual microbial taxa based on relative abundance data likely leads to false associations and conflicting discoveries in different studies. Moreover, the effects of underlying factors and microbe–microbe interactions could lead to the alteration of larger sets of taxa. It might be more robust to investigate gut microbiota using groups of related taxa instead of the composition of individual taxa. Results We proposed a novel method to identify underlying microbial modules, i.e. groups of taxa with similar abundance patterns affected by a common latent factor, from longitudinal gut microbiota and applied it to inflammatory bowel disease (IBD). The identified modules demonstrated closer intragroup relationships, indicating potential microbe–microbe interactions and influences of underlying factors. Associations between the modules and several clinical factors were investigated, especially disease states. The IBD-associated modules performed better in stratifying the subjects compared with the relative abundance of individual taxa. The modules were further validated in external cohorts, demonstrating the efficacy of the proposed method in identifying general and robust microbial modules. The study reveals the benefit of considering the ecological effects in gut microbiota analysis and the great promise of linking clinical factors with underlying microbial modules. Availability and implementation https://github.com/rwang-z/microbial_module.git.

MotivationThe confusion of acute inflammation infected by virus and bacteria or noninfectious inflammation will lead to missing the best therapy occasion resulting in poor prognoses. The diagnostic model based on host gene expression has been widely used to diagnose acute infections, but the clinical usage was hindered by the capability across different samples and cohorts due to the small sample size for signature training and discovery.ResultsHere, we construct a large-scale dataset integrating multiple host transcriptomic data and analyze it using a sophisticated strategy which removes batch effect and extracts the common information from different cohorts based on the relative expression alteration of gene pairs. We assemble 2680 samples across 16 cohorts and separately build gene pair signature (GPS) for bacterial, viral, and noninfected patients. The three GPSs are further assembled into an antibiotic decision model (bacterial–viral–noninfected GPS, bvnGPS) using multiclass neural networks, which is able to determine whether a patient is bacterial infected, viral infected, or noninfected. bvnGPS can distinguish bacterial infection with area under the receiver operating characteristic curve (AUC) of 0.953 (95% confidence interval, 0.948–0.958) and viral infection with AUC of 0.956 (0.951–0.961) in the test set (N = 760). In the validation set (N = 147), bvnGPS also shows strong performance by attaining an AUC of 0.988 (0.978–0.998) on bacterial-versus-other and an AUC of 0.994 (0.984–1.000) on viral-versus-other. bvnGPS has the potential to be used in clinical practice and the proposed procedure provides insight into data integration, feature selection and multiclass classification for host transcriptomics data.Availability and implementationThe codes implementing bvnGPS are available at https://github.com/Ritchiegit/bvnGPS. The construction of iPAGE algorithm and the training of neural network was conducted on Python 3.7 with Scikit-learn 0.24.1 and PyTorch 1.7. The visualization of the results was implemented on R 4.2, Python 3.7, and Matplotlib 3.3.4.

Motivation Many studies have shown that IDH mutation and 1p/19q co-deletion can serve as prognostic signatures of glioma. Although these genetic variations affect the expression of one or more genes, the prognostic value of gene expression related to IDH and 1p/19q status is still unclear. Results We constructed an ensemble gene pair signature for the risk evaluation and survival prediction of glioma based on the prior knowledge of the IDH and 1p/19q status. First, we separately built two gene pair signatures IDH-GPS and 1p/19q-GPS and elucidated that they were useful transcriptome markers projecting from corresponding genome variations. Then, the gene pairs in these two models were assembled to develop an integrated model named Glioma Prognostic Gene Pair Signature (GPGPS), which demonstrated high area under the curves (AUCs) to predict 1-, 3- and 5-year overall survival (0.92, 0.88 and 0.80) of glioma. GPGPS was superior to the single GPSs and other existing prognostic signatures (avg AUC = 0.70, concordance index = 0.74). In conclusion, the ensemble prognostic signature with 10 gene pairs could serve as an independent predictor for risk stratification and survival prediction in glioma. This study shed light on transferring knowledge from genetic alterations to expression changes to facilitate prognostic studies. Availability and implementation Codes are available at https://github.com/Kimxbzheng/GPGPS.git Supplementary information Supplementary data are available at Bioinformatics online.

BACKGROUND AND AIMS Alcohol consumption has been reported to impair the physical and mental health of the elderly. This study aimed to explore the association between alcohol consumption patterns in midlife and cognition in the elderly among the Chinese population. METHODS AND RESULTS Study subjects were individuals aged ≥45 years in the shared database of the China Health and Nutrition Survey in 1997, who were followed up in 2006. A questionnaire was used to collect information about alcohol consumption (frequency, amount and type). Alcohol consumption (grams/week) was classified into none, light (≤84), light-to-moderate (84.01-168), moderate-to-heavy (168.01-336) and heavy (≥336.01) categories in men, and none, light (

Advanced machine intelligence is empowered not only by the ever-increasing computational capability for information processing but also by sensors for collecting multimodal information from complex environments. However, simply assembling different sensors can result in bulky systems and complex data processing. Herein, we show that a complementary metal-oxide-semiconductor (CMOS) imager can be transformed into a compact multimodal sensing platform through dual-focus imaging. By combining lens-based and lensless imaging, visual information, chemicals, temperature, and humidity can be detected with the same chip and output as a single image. As a proof of concept, we equipped the sensor on a micro vehicle and demonstrated multimodal environmental sensing and mapping. We also developed a multimodal endoscope and achieved simultaneous imaging and chemical profiling along a porcine digestive tract. The multimodal CMOS imager is compact, versatile, and extensible and can be widely applied in microrobots, in vivo medical apparatuses, and other microdevices.

Publisher Copyright: © 2022 The non-coding RNA (ncRNA) regulation appears to be associated to the diagnosis and targeted therapy of complex diseases. Motifs of non-coding RNAs and genes in the competing endogenous RNA (ceRNA) network would probably contribute to the accurate prediction of serous ovarian carcinoma (SOC). We conducted a microarray study profiling the whole transcriptomes of eight human SOCs and eight controls and constructed a ceRNA network including mRNAs, long ncRNAs, and circular RNAs (circRNAs). Novel form of motifs (mRNA-ncRNA-mRNA) were identified from the ceRNA network and defined as non-coding RNA's competing endogenous gene pairs (ceGPs), using a proposed method denoised individualized pair analysis of gene expression (deiPAGE). 18 cricRNA's ceGPs (cceGPs) were identified from multiple cohorts and were fused as an indicator (SOC index) for SOC discrimination, which carried a high predictive capacity in independent cohorts. SOC index was negatively correlated with the CD8+/CD4+ ratio in tumour-infiltration, reflecting the migration and growth of tumour cells in ovarian cancer progression. Moreover, most of the RNAs in SOC index were experimentally validated involved in ovarian cancer development. Our results elucidate the discriminative capability of SOC index and suggest that the novel competing endogenous motifs play important roles in expression regulation and could be potential target for investigating ovarian cancer mechanism or its therapy.

Background Sepsis is the major cause of death in Intensive Care Unit (ICU) globally. Molecular detection enables rapid diagnosis that allows early intervention to minimize the death rate. Recent studies showed that long non-coding RNAs (lncRNAs) regulate proinflammatory genes and are related to the dysfunction of organs in sepsis. Identifying lncRNA signature with absolute abundance is challenging because of the technical variation and the systematic experimental bias. Results Cohorts (n = 768) containing whole blood lncRNA profiling of sepsis patients in the Gene Expression Omnibus (GEO) database were included. We proposed a novel diagnostic strategy that made use of the relative expressions of lncRNA pairs, which are reversed between sepsis patients and normal controls (eg. lncRNAi > lncRNAj in sepsis patients and lncRNAi j in normal controls), to identify 14 lncRNA pairs as a sepsis diagnostic signature. The signature was then applied to independent cohorts (n = 644) to evaluate its predictive performance across different ages and normalization methods. Comparing to common machine learning models and existing signatures, SepSigLnc consistently attains better performance on the validation cohorts from the same age group (AUC = 0.990 & 0.995 in two cohorts) and across different groups (AUC = 0.878 on average), as well as cohorts processed by an alternative normalization method (AUC = 0.953 on average). Functional analysis demonstrates that the lncRNA pairs in SepsigLnc are functionally similar and tend to implicate in the same biological processes including cell fate commitment and cellular response to steroid hormone stimulus. Conclusion Our study identified 14 lncRNA pairs as signature that can facilitate the diagnosis of septic patients at an intervenable point when clinical manifestations are not dramatic. Also, the computational procedure can be generalized to a standard procedure for discovering diagnostic molecule signatures.

A special functional group mediated functionalization platform is introduced as a new and versatile platform tool to improve the fluorescence detection performance of metal–organic frameworks (MOF). The creation of a mixed-functionalization strategy on a MOF realizes the high sensitivity detection of heavy metal ions, anions and small molecules. In this work, we have first reported a novel amino functionalized 3D indium MOF [In(BDC-NH2)(OH)]n (In1-NH2) which not only has an excellent fluorescent characteristic but also shows highly sensitive identification of Fe3+, Cu2+, Pb2+ and ClO− in water with broad linear ranges and short response times. Subsequently, based on the remaining amino group site of In1-NH2, a post-synthetic modification strategy is utilized to introduce an active boronic acid group for hydrogen peroxide detection. The obtained PBA-In1 exhibits an efficient sensing performance for hydrogen peroxide with an LOD of 0.42 μM. Given this, PBA-In1 is expected to become an effective probe to monitor the formation of metabolites in humans. In1-NH2 successfully achieves multiple ion detection and the PBA-In1 sensing platform with boronic acid functionalization may have good application prospects in biochemical research in the future.

It is very significant that functional porous metal-organic frameworks are used to manufacture hierarchical components to achieve cascading functions that cannot be achieved by a single-layer metal-organic framework (MOF). Here, we report two cases of novel MOFs constructed by the same ligand, Cu(I)-tpt and Cu(II)-tpt (Htpt = 5-[4(1H-1,2,4-triazol-1-yl)]phenyl-2H-tetrazole), and prepared a Cu(II)-tpt-on-Cu(I)-tpt membrane by a layer-by-layer approach ignoring the lattice mismatch problem. The first Cu(I)-tpt layer is grown on an oriented Cu2O nanostructured array by a "one-pot" approach. The aligned second Cu(II)-tpt layer can be deposited using liquid-phase epitaxy. Notably, the prepared Cu(II)-tpt-on-Cu(I)-tpt membrane combines adsorption and fluorescence sensing, which exhibited significant adsorption for Cr2O72- (203.25 mg g-1) as typical highly poisonous ions with a fluorescence quenching response. Hence, based on the oxidation-reduction between Cr2O72- and p-arsanilic acid (p-ASA), the Cu(II)-tpt-on-Cu(I)-tpt membrane's ability to adsorb Cr2O72- could be used to design "on-off-on" mode fluorescence probes to detect p-ASA with high sensitivity (limit of detection (LOD) = 0.0556 μg L-1). p-ASA can be degraded into highly toxic inorganic arsenic compounds in the natural environment and has received widespread attention. Therefore, the integration of adsorption and fluorescence properties makes the Cu(II)-tpt-on-Cu(I)-tpt membrane a feasible multifunctional material for pollution control and detection.

The non-coding RNA (ncRNA) regulation apprears to be associated to the diagnosis and targeted therapy of complex diseases. Motifs of non-coding RNAs and genes in the competing endogenous RNA (ceRNA) network would probably contribute to the accurate prediction of serous ovarian carcinoma (SOC). Hence, we profiled a whole transcriptome of eight human SOCs and eight controls and constructed a ceRNA network including mRNAs, long ncRNAs, and circular RNAs (circRNAs). We identified the mRNA–ncRNA–mRNA motifs in the ceRNA network named the non-coding RNA’s competing endogenous gene pairs (ceGPs), through the denoised individualized pair analysis of gene expression (deiPAGE) proposed in this study. 18 cricRNA’s ceGPs (cceGPs) were identified from multiple cohorts and were fused as an indicator (SOC index) for SOC discrimination, which carried a high predictive capacity in independent cohorts. It was found that the index was negatively correlated with the CD8+/CD4+ ratio in tumour-infiltration, reflecting the migration and growth of tumour cells in ovarian cancer progression.

Objectives: Numerous studies investigate the advantages of rapid molecular drug susceptibility testing (DST) in comparison to phenotypic DST, but the clinical impact on treating multi/extensively drug resistant TB(M/XDR-TB) is less studied. Therefore, we examined how molecular DST testing may improve MDR-TB treatment management and outcome in Chinese settings. Methods: We performed a comparative study of patient cohorts before and after the implementation of molecular DST diagnosis with Genotype MTBDRsl/MTBDRplus assay in two Chinese hospitals. We collected clinical information including time to sputum culture conversion and final treatment outcome. Results: In total, 242 MDR-TB patients were studied including 114 before (pre-implementation group) and 128 after the implementation (post-implementation group) of molecular DST. Time to MDR-TB diagnosis was significantly reduced for patients in the post-implementation group, as compared to the pre-implementation group (median,16 vs 62 days; P < 0.001). Patients with early available molecular DST results had a more rapid culture conversion (aHR1.94 95% CI: 1.37-2.73; median,12 vs 24 months, respectively; P < 0.001) and higher rate of treatment success (68% vs 47%, P < 0.01). Conclusions: The use of molecular DST in routine care for MDR-TB diagnosis as compared to phenotypic DST was associated with a decreased time to culture conversion and improved treatment outcome, highlighting its important clinical value.

Motivation Hepatocellular carcinoma (HCC) is a primary malignancy with a poor prognosis. Recently, multi-omics molecular-level measurement enables HCC diagnosis and prognosis prediction, which is crucial for early intervention of personalized therapy to diminish mortality. Here, we introduce a novel strategy utilizing DNA methylation and RNA expression data to achieve a multi-omics gene pair signature (GPS) for HCC discrimination. Results The immune genes with negative correlations between expression and promoter methylation are enriched in the highly connected cancer-related pathway network, which are considered as the candidates for HCC detection. After that, we separately construct a methylation GPS (mGPS) and an expression GPS (eGPS), and then assemble them as a meGPS with five gene pairs, in which the significant methylation and expression changes occur between HCC tumor and non-tumor groups. Reliable performance has been validated by independent tissue (age, gender and etiology) and blood datasets. This study proposes a procedure for multi-omics GPS identification and develops a novel HCC signature using both methylome and transcriptome data, suggesting potential molecular targets for the detection and therapy of HCC. Availability and implementation Models are available at https://github.com/bioinformaticStudy/meGPS.git. Supplementary information Supplementary data are available at Bioinformatics online.

The advances in single-cell RNA sequencing (scRNA-seq) technologies enable the characterization of transcriptomic profiles at the cellular level and demonstrate great promise in bulk sample analysis thereby offering opportunities to transfer gene signature from scRNA-seq to bulk data. However, the gene expression signatures identified from single cells are typically inapplicable to bulk RNA-seq data due to the profiling differences of distinct sequencing technologies. Here, we propose single-cell pair-wise gene expression (scPAGE), a novel method to develop single-cell gene pair signatures (scGPSs) that were beneficial to bulk RNA-seq classification to transfer knowledge across platforms. PAGE was adopted to tackle the challenge of profiling differences. We applied the method to acute myeloid leukemia (AML) and identified the scGPS from mouse scRNA-seq that allowed discriminating between AML and control cells. The scGPS was validated in bulk RNA-seq datasets and demonstrated better performance (average area under the curve [AUC] = 0.96) than the conventional gene expression strategies (average AUC$\le$ 0.88) suggesting its potential in disclosing the molecular mechanism of AML. The scGPS also outperformed its bulk counterpart, which highlighted the benefit of gene signature transfer. Furthermore, we confirmed the utility of scPAGE in sepsis as an example of other disease scenarios. scPAGE leveraged the advantages of single-cell profiles to enhance the analysis of bulk samples revealing great potential of transferring knowledge from single-cell to bulk transcriptome studies.

BackgroundUnderstanding the impact of drug exposure and susceptibility on treatment response of multidrug-resistant tuberculosis (MDR-TB) will help to optimise treatment. This study aimed to investigate the association between drug exposure, susceptibility and response to MDR-TB treatment.MethodsDrug exposure and susceptibility for second-line drugs were measured for patients with MDR-TB. Multivariate analysis was applied to investigate the impact of drug exposure and susceptibility on sputum culture conversion and treatment outcome. Probability of target attainment was evaluated. Random Forest and CART (Classification and Regression Tree) analysis was used to identify key predictors and their clinical targets among patients on World Health Organization-recommended regimens.ResultsDrug exposure and corresponding susceptibility were available for 197 patients with MDR-TB. The probability of target attainment was highly variable, ranging from 0% for ethambutol to 97% for linezolid, while patients with fluoroquinolones above targets had a higher probability of 2-month culture conversion (56.3% versus 28.6%; adjusted OR 2.91, 95% CI 1.42–5.94) and favourable outcome (88.8% versus 68.8%; adjusted OR 2.89, 95% CI 1.16–7.17). Higher exposure values of fluoroquinolones, linezolid and pyrazinamide were associated with earlier sputum culture conversion. CART analysis selected moxifloxacin area under the drug concentration–time curve/minimum inhibitory concentration (AUC0–24h/MIC) of 231 and linezolid AUC0–24h/MIC of 287 as best predictors for 6-month culture conversion in patients receiving identical Group A-based regimens. These associations were confirmed in multivariate analysis.ConclusionsOur findings indicate that target attainment of TB drugs is associated with response to treatment. The CART-derived thresholds may serve as targets for early dose adjustment in a future randomised controlled study to improve MDR-TB treatment outcome.

Anthrax spores have been a determining risk to human beings and animals across the world. As such, the qualitative monitoring toward dipicolinic acid (DPA), the unique biomarker of bacillus anthracis, is highly desired and being moved forward in health management. Herein, a breathing zeolite-like supramolecular assembly (ZSA), with the

Spiro-OMeTAD is currently the most widely used hole transport material for the preparation of high-performance perovskite solar cells (PSCs), usually requiring the addition of additives to achieve the desired electronic conductivity. However, the quality of the film is degraded owing to the addition of additives. Holes and defects can be observed, and the dispersion of the additives are uneven inside. Here, a copper coordination polymer, Cu-bix, with matching energy level and fluorescent properties was screened for use as an additional additive to dope Spiro-OMeTAD. The doping of Cu-bix effectively improved the dispersion state of the additives in the hole transport layers and alleviated the aggregation of LiTFSI (lithium bis(trifluoromethanesulfonyl)imide) or/and lithium salt complexes in the film. Owing to better dispersion of the additives, Spiro-OMeTAD was more fully and uniformly oxidized whereas the possibility of charge recombination was reduced in the devices. Furthermore, the flat and tightly bonded film layer obtained by optimization of the doping amount can efficiently transfer holes from the perovskite layers to the hole transport layers. Possible interaction mechanisms between additives and the copper coordination polymer are proposed and discussed. The resulting power conversion efficiency (PCE) for Cu-bix-doped PSCs was improved from 16.52 % to 18.47 % compared to the pristine devices, and this type of PSCs also showed a long stability in air owing to the increased hydrophobicity of the Cu-bix-based hole transport layers.

Three metal–organic complexes crystal materials (MOC-1, MOC-2, and MOC-3) have been hydrothermally synthesized. Driven by C–H···O and C–H···Cl hydrogen bonding interactions, MOC-1, MOC-2, and MOC-3...

Seven Schiff base fluorescent sensors (L1−L7) with multiple-color aggregation-induced emission (AIE) properties for detection of Hg2+ ions and pH integrating Boolean logic operations were reported. With appropriate ligand-to-metal coordination (L−HgCl2) and AIE characteristic, L−HgCl2 can aggregate into nanoparticles, followed by activation of strong emission, therefore, the “turn-on” assay for detection of Hg2+ can be established. Sensing mechanism was calculated through single-crystal X-ray diffraction, Job’s Plot, Benesi−Hildebrand equation, dynamic light scattering, scanning electron microscopy, and elemental mapping analysis. Density functional theory calculations revealed that L−HgCl2 can avoid one-electron transfer and retain bright luminescence compared with that of L. Different (highest unoccupied orbit) HOMO and (Lowest empty orbit) LUMO energy gaps enable the different electron transitions, resulting in the multiple-color fluorescence properties. Especially, L1−L7 exhibit pH-dependent fluorescent characteristics: different electron-donating ability of the substituent groups for L1−L7 show different titration jump of pH. Advanced level molecular logic gates have been constructed integrating the sensing of Hg2+ and pH. More importantly, the L-coated test paper applied in Hg2+ sensing makes tracing possible, which is convenient for confidential purposes.

Fluorescent materials with lanthanide cations encapsulated in MOFs are currently used in numerous applications, especially in biosensors. Therefore, herein, two novel composites were designed and developed based on a Tb(III)Y moreover, it was used as an excellent ratiometric fluorescent sensor for the metabolic product NMF of DMF in the human body (LOD = 0.02 μM). In addition, the Yb3+ ions were doped into Tb@Cu-Hcbpp to improve the fluorescence performance of the green Tb3+ ion emission. Among the series of Tb1−xYbx@Cu-Hcbpp samples (x = 0.05, 0.10, 0.15, 0.20, 0.25, 0.30, 0.35 and 0.40), Tb0.85Yb0.15@Cu-Hcbpp showed maximum enhanced fluorescence intensity (almost 9.6 times that of the pure terbium system), but exhibited high fluorescence quenching efficiency for methylglyoxal (MGO), which could be used for the sensitive detection of MGO (LOD = 0.25 μM). Furthermore, the developed biosensors were successfully applied for the detection of NMF and MGO in urine and serum samples, and satisfactory results were obtained, showing good potential of these biosensors in practical applications such as in disease diagnosis and biochemical research.

It is highly challenging to create a novel fluorescent material that can quickly and credibly differentiate between polluting ions and organic matter in water monitoring. Here, a dual-emissive fluorescent platform based on a novel RhB-CDs@1 (RhB = rhodamine B, CDs = carbon dots, 1 = [Cu2L(OH−)]n, L = 1,3-bis(4′-carboxylic acid benzyloxy)benzoic acid) composite is constructed by introducing CDs and RhB into metal–organic frameworks (MOFs). The RhB-CDs@1 probe acts as a dual-emitting ratio sensor that not only avoids the errors caused by environmental and instrumental instability, but also has the advantages of MOFs that can aggregate the analytes. Interestingly, this probe has different responses to different types of antibiotic, and it especially shows rapid and sensitive identification of quinolones, tetracyclines and nitrofurans in water (the detection limit is as low as 25 ppb for ciprofloxacin; 44 ppb for norfloxacin; 65 ppb for nitrofurazone; 74 ppb for nitrofurantoin; 777 ppb for tetracycline). Similarly, an efficient ratiometric fluorescent probe for MnO4− was designed based on different fluorescence responses to MnO4− ions in different acid and base conditions (MnO4−, H+ and OH−), and a Boolean logic operation was constructed. All of the results indicate that the RhB-CDs@1 probes have merits of outstanding specific surface area and strong molecular/ionic recognition that are promising for the simultaneous selective recognition and detection of antibiotics or MnO4−, providing application prospects in the monitoring of water quality.

A special functional group mediated functionalization platform is introduced as a new and versatile platform tool to improve the fluorescence detection performance of metal-organic frameworks (MOF). The creation of a mixed-functionalization strategy on a MOF realizes the high sensitivity detection of heavy metal ions, anions and small molecules. In this work, we have first reported a novel amino functionalized 3D indium MOF [In(BDC-NH2)(OH)]n (In1-NH2) which not only has an excellent fluorescent characteristic but also shows highly sensitive identification of Fe3+, Cu2+, Pb2+ and ClO- in water with broad linear ranges and short response times. Subsequently, based on the remaining amino group site of In1-NH2, a post-synthetic modification strategy is utilized to introduce an active boronic acid group for hydrogen peroxide detection. The obtained PBA-In1 exhibits an efficient sensing performance for hydrogen peroxide with an LOD of 0.42 μM. Given this, PBA-In1 is expected to become an effective probe to monitor the formation of metabolites in humans. In1-NH2 successfully achieves multiple ion detection and the PBA-In1 sensing platform with boronic acid functionalization may have good application prospects in biochemical research in the future.